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[GH-ISSUE #13552] Ollama slow performance. #34686

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opened 2026-04-22 18:26:26 -05:00 by GiteaMirror · 21 comments
GiteaMirror commented 2026-04-22 18:26:26 -05:00
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Originally created by @Bcwz on GitHub (Dec 23, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/13552

What is the issue?

I'm using the following models and Ollama is taking quite some time to response to a simple http /api/chat request.
I'm using 2x Nvidia DGX Spark.

Running Ollama using Docker (docker run -d --gpus=all -v "$(pwd)/app-data:/root/.ollama" -p 11434:11434 --name local-ollama-gpu ollama/ollama)

  1. gpt-oss:20b
  2. phi4:14b
  3. gemma3:12b
  4. qwen3:8b
  5. granite3.3:8b

Tried reinstalling Ollama, redownloading the models, restarting the system and still same issue.

It used to be running a bit faster, but after awhile (20~30mins) it starts to become very slow to response.
I am expecting Ollama to response much faster given the hardware and small model size.
Any ways to make the models response under a min instead of > 10mins per request?

Relevant log output

docker exec -it local-ollama-gpu nvidia-smi
Tue Dec 23 11:13:46 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GB10                    On  |   0000000F:01:00.0  On |                  N/A |
| N/A   78C    P0             47W /  N/A  | Not Supported          |     92%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A             248      C   /usr/bin/ollama                        8909MiB |
|    0   N/A  N/A             295      C   /usr/bin/ollama                       12678MiB |
+-----------------------------------------------------------------------------------------+

time=2025-12-23T10:26:21.168Z level=INFO source=routes.go:1544 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GGML_VK_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_REMOTES:[ollama.com] OLLAMA_SCHED_SPREAD:false OLLAMA_VULKAN:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
time=2025-12-23T10:26:21.168Z level=INFO source=images.go:522 msg="total blobs: 0"
time=2025-12-23T10:26:21.168Z level=INFO source=images.go:529 msg="total unused blobs removed: 0"
time=2025-12-23T10:26:21.168Z level=INFO source=routes.go:1597 msg="Listening on [::]:11434 (version 0.13.2)"
time=2025-12-23T10:26:21.169Z level=INFO source=runner.go:67 msg="discovering available GPUs..."
time=2025-12-23T10:26:21.169Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 39717"
time=2025-12-23T10:26:21.459Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 38293"
time=2025-12-23T10:26:21.804Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 42057"
time=2025-12-23T10:26:21.804Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 43237"
time=2025-12-23T10:26:22.175Z level=INFO source=types.go:42 msg="inference compute" id=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca filter_id="" library=CUDA compute=12.1 name=CUDA0 description="NVIDIA GB10" libdirs=ollama,cuda_v13 driver=13.0 pci_id=000f:01:00.0 type=iGPU total="119.6 GiB" available="112.9 GiB"
[GIN] 2025/12/23 - 10:26:44 | 200 |       38.32µs |       127.0.0.1 | HEAD     "/"
time=2025-12-23T10:26:45.911Z level=INFO source=download.go:177 msg="downloading e7b273f96360 in 16 862 MB part(s)"
[GIN] 2025/12/23 - 10:27:21 | 200 |       25.52µs |       127.0.0.1 | HEAD     "/"
time=2025-12-23T10:27:23.207Z level=INFO source=download.go:177 msg="downloading 77bcee066a76 in 16 308 MB part(s)"
[GIN] 2025/12/23 - 10:28:07 | 200 |      34.976µs |       127.0.0.1 | HEAD     "/"
time=2025-12-23T10:28:09.354Z level=INFO source=download.go:177 msg="downloading e8ad13eff07a in 16 509 MB part(s)"
[GIN] 2025/12/23 - 10:28:26 | 200 |       27.92µs |       127.0.0.1 | HEAD     "/"
time=2025-12-23T10:28:27.895Z level=INFO source=download.go:177 msg="downloading a3de86cd1c13 in 16 326 MB part(s)"
[GIN] 2025/12/23 - 10:28:53 | 200 |      28.672µs |       127.0.0.1 | HEAD     "/"
time=2025-12-23T10:28:55.215Z level=INFO source=download.go:177 msg="downloading fd7b6731c33c in 16 565 MB part(s)"
time=2025-12-23T10:31:05.552Z level=INFO source=download.go:177 msg="downloading 3da071a01bbe in 1 6.6 KB part(s)"
time=2025-12-23T10:31:07.400Z level=INFO source=download.go:177 msg="downloading 4a99a6dd617d in 1 11 KB part(s)"
time=2025-12-23T10:31:09.320Z level=INFO source=download.go:177 msg="downloading 122661774644 in 1 417 B part(s)"
[GIN] 2025/12/23 - 10:31:14 | 200 |         3m52s |       127.0.0.1 | POST     "/api/pull"
time=2025-12-23T10:31:57.577Z level=INFO source=download.go:177 msg="downloading ae370d884f10 in 1 1.7 KB part(s)"
time=2025-12-23T10:31:59.365Z level=INFO source=download.go:177 msg="downloading d18a5cc71b84 in 1 11 KB part(s)"
time=2025-12-23T10:32:01.026Z level=INFO source=download.go:177 msg="downloading cff3f395ef37 in 1 120 B part(s)"
time=2025-12-23T10:32:02.664Z level=INFO source=download.go:177 msg="downloading 05a61d37b084 in 1 487 B part(s)"
[GIN] 2025/12/23 - 10:32:06 | 200 |         3m40s |       127.0.0.1 | POST     "/api/pull"
time=2025-12-23T10:32:27.601Z level=INFO source=download.go:177 msg="downloading fa6710a93d78 in 1 7.2 KB part(s)"
time=2025-12-23T10:32:29.248Z level=INFO source=download.go:177 msg="downloading f60356777647 in 1 11 KB part(s)"
time=2025-12-23T10:32:30.926Z level=INFO source=download.go:177 msg="downloading d8ba2f9a17b3 in 1 18 B part(s)"
time=2025-12-23T10:32:32.161Z level=INFO source=download.go:177 msg="downloading e0a42594d802 in 1 358 B part(s)"
time=2025-12-23T10:32:32.548Z level=INFO source=download.go:177 msg="downloading 776beb3adb23 in 1 489 B part(s)"
time=2025-12-23T10:32:33.768Z level=INFO source=download.go:177 msg="downloading dd084c7d92a3 in 1 8.4 KB part(s)"
time=2025-12-23T10:32:35.351Z level=INFO source=download.go:177 msg="downloading 3116c5225075 in 1 77 B part(s)"
time=2025-12-23T10:32:36.787Z level=INFO source=download.go:177 msg="downloading 32695b892af8 in 1 275 B part(s)"
time=2025-12-23T10:32:36.951Z level=INFO source=download.go:177 msg="downloading 6819964c2bcf in 1 490 B part(s)"
time=2025-12-23T10:32:38.368Z level=INFO source=download.go:177 msg="downloading fa8235e5b48f in 1 1.1 KB part(s)"
time=2025-12-23T10:32:39.929Z level=INFO source=download.go:177 msg="downloading 45a1c652dddc in 1 82 B part(s)"
[GIN] 2025/12/23 - 10:32:41 | 200 |         5m56s |       127.0.0.1 | POST     "/api/pull"
time=2025-12-23T10:32:41.503Z level=INFO source=download.go:177 msg="downloading f5d6f49c6477 in 1 486 B part(s)"
[GIN] 2025/12/23 - 10:32:42 | 200 |         4m34s |       127.0.0.1 | POST     "/api/pull"
[GIN] 2025/12/23 - 10:32:47 | 200 |         3m53s |       127.0.0.1 | POST     "/api/pull"
time=2025-12-23T10:33:42.676Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 35353"
time=2025-12-23T10:33:42.967Z level=WARN source=cpu_linux.go:130 msg="failed to parse CPU allowed micro secs" error="strconv.ParseInt: parsing \"max\": invalid syntax"
llama_model_loader: loaded meta data with 33 key-value pairs and 243 tensors from /root/.ollama/models/blobs/sha256-fd7b6731c33c57f61767612f56517460ec2d1e2e5a3f0163e0eb3d8d8cb5df20 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = phi3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Phi 4
llama_model_loader: - kv   3:                            general.version str              = 4
llama_model_loader: - kv   4:                       general.organization str              = Microsoft
llama_model_loader: - kv   5:                           general.basename str              = phi
llama_model_loader: - kv   6:                         general.size_label str              = 15B
llama_model_loader: - kv   7:                            general.license str              = mit
llama_model_loader: - kv   8:                       general.license.link str              = https://huggingface.co/microsoft/phi-...
llama_model_loader: - kv   9:                               general.tags arr[str,7]       = ["phi", "nlp", "math", "code", "chat"...
llama_model_loader: - kv  10:                          general.languages arr[str,1]       = ["en"]
llama_model_loader: - kv  11:                        phi3.context_length u32              = 16384
llama_model_loader: - kv  12:  phi3.rope.scaling.original_context_length u32              = 16384
llama_model_loader: - kv  13:                      phi3.embedding_length u32              = 5120
llama_model_loader: - kv  14:                   phi3.feed_forward_length u32              = 17920
llama_model_loader: - kv  15:                           phi3.block_count u32              = 40
llama_model_loader: - kv  16:                  phi3.attention.head_count u32              = 40
llama_model_loader: - kv  17:               phi3.attention.head_count_kv u32              = 10
llama_model_loader: - kv  18:      phi3.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  19:                  phi3.rope.dimension_count u32              = 128
llama_model_loader: - kv  20:                        phi3.rope.freq_base f32              = 250000.000000
llama_model_loader: - kv  21:                          general.file_type u32              = 15
llama_model_loader: - kv  22:              phi3.attention.sliding_window u32              = 131072
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = dbrx
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,100352]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,100352]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,100000]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  28:                tokenizer.ggml.bos_token_id u32              = 100257
llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 100257
llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 100257
llama_model_loader: - kv  31:                    tokenizer.chat_template str              = {% for message in messages %}{% if (m...
llama_model_loader: - kv  32:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   81 tensors
llama_model_loader: - type q4_K:  101 tensors
llama_model_loader: - type q5_K:   40 tensors
llama_model_loader: - type q6_K:   21 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 8.43 GiB (4.94 BPW) 
load: printing all EOG tokens:
load:   - 100257 ('<|endoftext|>')
load:   - 100265 ('<|im_end|>')
load: special tokens cache size = 96
load: token to piece cache size = 0.6151 MB
print_info: arch             = phi3
print_info: vocab_only       = 1
print_info: model type       = ?B
print_info: model params     = 14.66 B
print_info: general.name     = Phi 4
print_info: vocab type       = BPE
print_info: n_vocab          = 100352
print_info: n_merges         = 100000
print_info: BOS token        = 100257 '<|endoftext|>'
print_info: EOS token        = 100257 '<|endoftext|>'
print_info: EOT token        = 100265 '<|im_end|>'
print_info: PAD token        = 100257 '<|endoftext|>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 100258 '<|fim_prefix|>'
print_info: FIM SUF token    = 100260 '<|fim_suffix|>'
print_info: FIM MID token    = 100259 '<|fim_middle|>'
print_info: EOG token        = 100257 '<|endoftext|>'
print_info: EOG token        = 100265 '<|im_end|>'
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2025-12-23T10:33:43.060Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --model /root/.ollama/models/blobs/sha256-fd7b6731c33c57f61767612f56517460ec2d1e2e5a3f0163e0eb3d8d8cb5df20 --port 37983"
time=2025-12-23T10:33:43.060Z level=INFO source=sched.go:443 msg="system memory" total="119.6 GiB" free="80.0 GiB" free_swap="16.0 GiB"
time=2025-12-23T10:33:43.060Z level=INFO source=sched.go:450 msg="gpu memory" id=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca library=CUDA available="112.2 GiB" free="112.6 GiB" minimum="457.0 MiB" overhead="0 B"
time=2025-12-23T10:33:43.060Z level=INFO source=server.go:459 msg="loading model" "model layers"=41 requested=-1
time=2025-12-23T10:33:43.061Z level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="8.2 GiB"
time=2025-12-23T10:33:43.061Z level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="800.0 MiB"
time=2025-12-23T10:33:43.061Z level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="533.3 MiB"
time=2025-12-23T10:33:43.061Z level=INFO source=device.go:272 msg="total memory" size="9.5 GiB"
time=2025-12-23T10:33:43.067Z level=INFO source=runner.go:963 msg="starting go runner"
load_backend: loaded CPU backend from /usr/lib/ollama/libggml-cpu.so
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, ID: GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca
load_backend: loaded CUDA backend from /usr/lib/ollama/cuda_v13/libggml-cuda.so
time=2025-12-23T10:33:43.273Z level=INFO source=ggml.go:104 msg=system CPU.0.NEON=1 CPU.0.ARM_FMA=1 CPU.0.LLAMAFILE=1 CPU.1.NEON=1 CPU.1.ARM_FMA=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2025-12-23T10:33:43.273Z level=INFO source=runner.go:999 msg="Server listening on 127.0.0.1:37983"
time=2025-12-23T10:33:43.276Z level=INFO source=runner.go:893 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:false KvSize:4096 KvCacheType: NumThreads:20 GPULayers:41[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:41(0..40)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:true}"
time=2025-12-23T10:33:43.277Z level=INFO source=server.go:1294 msg="waiting for llama runner to start responding"
time=2025-12-23T10:33:43.277Z level=INFO source=server.go:1328 msg="waiting for server to become available" status="llm server loading model"
ggml_nvml_get_device_memory NVML not supported for memory query, using system memory (total=128457203712, available=120889769984)
ggml_backend_cuda_device_get_memory device GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca utilizing NVML memory reporting free: 120889769984 total: 128457203712
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 115289 MiB free
llama_model_loader: loaded meta data with 33 key-value pairs and 243 tensors from /root/.ollama/models/blobs/sha256-fd7b6731c33c57f61767612f56517460ec2d1e2e5a3f0163e0eb3d8d8cb5df20 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = phi3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Phi 4
llama_model_loader: - kv   3:                            general.version str              = 4
llama_model_loader: - kv   4:                       general.organization str              = Microsoft
llama_model_loader: - kv   5:                           general.basename str              = phi
llama_model_loader: - kv   6:                         general.size_label str              = 15B
llama_model_loader: - kv   7:                            general.license str              = mit
llama_model_loader: - kv   8:                       general.license.link str              = https://huggingface.co/microsoft/phi-...
llama_model_loader: - kv   9:                               general.tags arr[str,7]       = ["phi", "nlp", "math", "code", "chat"...
llama_model_loader: - kv  10:                          general.languages arr[str,1]       = ["en"]
llama_model_loader: - kv  11:                        phi3.context_length u32              = 16384
llama_model_loader: - kv  12:  phi3.rope.scaling.original_context_length u32              = 16384
llama_model_loader: - kv  13:                      phi3.embedding_length u32              = 5120
llama_model_loader: - kv  14:                   phi3.feed_forward_length u32              = 17920
llama_model_loader: - kv  15:                           phi3.block_count u32              = 40
llama_model_loader: - kv  16:                  phi3.attention.head_count u32              = 40
llama_model_loader: - kv  17:               phi3.attention.head_count_kv u32              = 10
llama_model_loader: - kv  18:      phi3.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  19:                  phi3.rope.dimension_count u32              = 128
llama_model_loader: - kv  20:                        phi3.rope.freq_base f32              = 250000.000000
llama_model_loader: - kv  21:                          general.file_type u32              = 15
llama_model_loader: - kv  22:              phi3.attention.sliding_window u32              = 131072
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = dbrx
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,100352]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,100352]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,100000]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  28:                tokenizer.ggml.bos_token_id u32              = 100257
llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 100257
llama_model_loader: - kv  30:            tokenizer.ggml.padding_token_id u32              = 100257
llama_model_loader: - kv  31:                    tokenizer.chat_template str              = {% for message in messages %}{% if (m...
llama_model_loader: - kv  32:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   81 tensors
llama_model_loader: - type q4_K:  101 tensors
llama_model_loader: - type q5_K:   40 tensors
llama_model_loader: - type q6_K:   21 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 8.43 GiB (4.94 BPW) 
load_hparams: Phi SWA is currently disabled - results might be suboptimal for some models (see https://github.com/ggml-org/llama.cpp/pull/13676)
load: printing all EOG tokens:
load:   - 100257 ('<|endoftext|>')
load:   - 100265 ('<|im_end|>')
load: special tokens cache size = 96
load: token to piece cache size = 0.6151 MB
print_info: arch             = phi3
print_info: vocab_only       = 0
print_info: n_ctx_train      = 16384
print_info: n_embd           = 5120
print_info: n_embd_inp       = 5120
print_info: n_layer          = 40
print_info: n_head           = 40
print_info: n_head_kv        = 10
print_info: n_rot            = 128
print_info: n_swa            = 0
print_info: is_swa_any       = 0
print_info: n_embd_head_k    = 128
print_info: n_embd_head_v    = 128
print_info: n_gqa            = 4
print_info: n_embd_k_gqa     = 1280
print_info: n_embd_v_gqa     = 1280
print_info: f_norm_eps       = 0.0e+00
print_info: f_norm_rms_eps   = 1.0e-05
print_info: f_clamp_kqv      = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale    = 0.0e+00
print_info: f_attn_scale     = 0.0e+00
print_info: n_ff             = 17920
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: n_expert_groups  = 0
print_info: n_group_used     = 0
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 2
print_info: rope scaling     = linear
print_info: freq_base_train  = 250000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn  = 16384
print_info: rope_finetuned   = unknown
print_info: model type       = 14B
print_info: model params     = 14.66 B
print_info: general.name     = Phi 4
print_info: vocab type       = BPE
print_info: n_vocab          = 100352
print_info: n_merges         = 100000
print_info: BOS token        = 100257 '<|endoftext|>'
print_info: EOS token        = 100257 '<|endoftext|>'
print_info: EOT token        = 100265 '<|im_end|>'
print_info: PAD token        = 100257 '<|endoftext|>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 100258 '<|fim_prefix|>'
print_info: FIM SUF token    = 100260 '<|fim_suffix|>'
print_info: FIM MID token    = 100259 '<|fim_middle|>'
print_info: EOG token        = 100257 '<|endoftext|>'
print_info: EOG token        = 100265 '<|im_end|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 40 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 41/41 layers to GPU
load_tensors:   CPU_Mapped model buffer size =   275.62 MiB
load_tensors:        CUDA0 model buffer size =  8354.71 MiB
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 4096
llama_context: n_ctx_seq     = 4096
llama_context: n_batch       = 512
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = disabled
llama_context: kv_unified    = false
llama_context: freq_base     = 250000.0
llama_context: freq_scale    = 1
llama_context: n_ctx_seq (4096) < n_ctx_train (16384) -- the full capacity of the model will not be utilized
llama_context:  CUDA_Host  output buffer size =     0.40 MiB
llama_kv_cache:      CUDA0 KV buffer size =   800.00 MiB
llama_kv_cache: size =  800.00 MiB (  4096 cells,  40 layers,  1/1 seqs), K (f16):  400.00 MiB, V (f16):  400.00 MiB
llama_context:      CUDA0 compute buffer size =   353.01 MiB
llama_context:  CUDA_Host compute buffer size =    23.01 MiB
llama_context: graph nodes  = 1366
llama_context: graph splits = 2
time=2025-12-23T10:34:16.929Z level=INFO source=server.go:1332 msg="llama runner started in 33.87 seconds"
time=2025-12-23T10:34:16.929Z level=INFO source=sched.go:517 msg="loaded runners" count=1
time=2025-12-23T10:34:16.929Z level=INFO source=server.go:1294 msg="waiting for llama runner to start responding"
time=2025-12-23T10:34:16.929Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 46307"
time=2025-12-23T10:34:16.930Z level=INFO source=server.go:1332 msg="llama runner started in 33.87 seconds"
time=2025-12-23T10:34:17.388Z level=WARN source=cpu_linux.go:130 msg="failed to parse CPU allowed micro secs" error="strconv.ParseInt: parsing \"max\": invalid syntax"
time=2025-12-23T10:34:17.404Z level=INFO source=sched.go:583 msg="updated VRAM based on existing loaded models" gpu=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca library=CUDA total="119.6 GiB" available="102.1 GiB"
time=2025-12-23T10:34:17.457Z level=INFO source=server.go:209 msg="enabling flash attention"
time=2025-12-23T10:34:17.458Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --model /root/.ollama/models/blobs/sha256-a3de86cd1c132c822487ededd47a324c50491393e6565cd14bafa40d0b8e686f --port 35313"
time=2025-12-23T10:34:17.458Z level=INFO source=sched.go:443 msg="system memory" total="119.6 GiB" free="79.0 GiB" free_swap="16.0 GiB"
time=2025-12-23T10:34:17.458Z level=INFO source=sched.go:450 msg="gpu memory" id=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca library=CUDA available="101.7 GiB" free="102.1 GiB" minimum="457.0 MiB" overhead="0 B"
time=2025-12-23T10:34:17.458Z level=INFO source=server.go:702 msg="loading model" "model layers"=37 requested=-1
time=2025-12-23T10:34:17.472Z level=INFO source=runner.go:1398 msg="starting ollama engine"
time=2025-12-23T10:34:17.472Z level=INFO source=runner.go:1433 msg="Server listening on 127.0.0.1:35313"
time=2025-12-23T10:34:17.480Z level=INFO source=runner.go:1271 msg=load request="{Operation:fit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:true KvSize:4096 KvCacheType: NumThreads:20 GPULayers:37[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:37(0..36)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2025-12-23T10:34:17.505Z level=INFO source=ggml.go:136 msg="" architecture=qwen3 file_type=Q4_K_M name="Qwen3 8B" description="" num_tensors=399 num_key_values=29
load_backend: loaded CPU backend from /usr/lib/ollama/libggml-cpu.so
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, ID: GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca
load_backend: loaded CUDA backend from /usr/lib/ollama/cuda_v13/libggml-cuda.so
time=2025-12-23T10:34:18.078Z level=INFO source=ggml.go:104 msg=system CPU.0.NEON=1 CPU.0.ARM_FMA=1 CPU.0.LLAMAFILE=1 CPU.1.NEON=1 CPU.1.ARM_FMA=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2025-12-23T10:34:24.803Z level=INFO source=runner.go:1271 msg=load request="{Operation:alloc LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:true KvSize:4096 KvCacheType: NumThreads:20 GPULayers:37[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:37(0..36)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2025-12-23T10:34:25.322Z level=INFO source=runner.go:1271 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:true KvSize:4096 KvCacheType: NumThreads:20 GPULayers:37[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:37(0..36)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2025-12-23T10:34:25.322Z level=INFO source=ggml.go:482 msg="offloading 36 repeating layers to GPU"
time=2025-12-23T10:34:25.322Z level=INFO source=ggml.go:489 msg="offloading output layer to GPU"
time=2025-12-23T10:34:25.322Z level=INFO source=ggml.go:494 msg="offloaded 37/37 layers to GPU"
time=2025-12-23T10:34:25.322Z level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="4.5 GiB"
time=2025-12-23T10:34:25.322Z level=INFO source=device.go:245 msg="model weights" device=CPU size="333.8 MiB"
time=2025-12-23T10:34:25.322Z level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="576.0 MiB"
time=2025-12-23T10:34:25.322Z level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="126.0 MiB"
time=2025-12-23T10:34:25.322Z level=INFO source=device.go:267 msg="compute graph" device=CPU size="8.0 MiB"
time=2025-12-23T10:34:25.322Z level=INFO source=device.go:272 msg="total memory" size="5.6 GiB"
time=2025-12-23T10:34:25.323Z level=INFO source=sched.go:517 msg="loaded runners" count=2
time=2025-12-23T10:34:25.323Z level=INFO source=server.go:1294 msg="waiting for llama runner to start responding"
time=2025-12-23T10:34:25.323Z level=INFO source=server.go:1328 msg="waiting for server to become available" status="llm server loading model"
time=2025-12-23T10:34:26.076Z level=INFO source=server.go:1332 msg="llama runner started in 8.62 seconds"
[GIN] 2025/12/23 - 10:34:40 | 200 | 57.956598944s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:36:24 | 200 |         1m43s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:37:18 | 200 |         3m24s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:37:20 | 200 | 56.430880799s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:37:36 | 200 |         3m21s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:38:33 | 200 |         1m50s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:38:44 | 200 |         1m23s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:40:36 | 200 |         3m18s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:40:50 | 200 |         2m17s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:40:57 | 200 |         3m21s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:42:11 | 200 |         2m28s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:42:28 | 200 |         1m37s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:45:17 | 200 |         4m41s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:45:20 | 200 |         4m22s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:45:31 | 200 |         3m19s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:45:41 | 200 |         3m12s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:47:01 | 200 |         4m18s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:47:36 | 200 |          2m5s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:48:09 | 200 |         2m27s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:48:31 | 200 |         3m13s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:48:34 | 200 |         3m13s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:50:19 | 200 |         3m18s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:50:24 | 200 |         2m48s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:51:13 | 200 |          3m3s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:52:00 | 200 |         3m18s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:52:24 | 200 |          2m4s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:52:44 | 200 |         1m31s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:53:46 | 200 |          2m4s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:53:59 | 200 |          3m9s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:54:22 | 200 |         1m33s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:54:34 | 200 |         2m28s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:54:44 | 200 |         2m20s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:54:53 | 200 |          2m9s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:55:40 | 200 |         1m48s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:56:14 | 200 |         1m35s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:57:21 | 200 |         2m38s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:58:33 | 200 |         2m53s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 10:59:36 | 200 |         3m21s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:01:01 | 200 |         3m35s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:02:18 | 200 |         4m35s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:02:34 | 200 |          4m0s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:02:46 | 200 |         3m10s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:03:40 | 200 |         2m58s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:04:43 | 200 |         3m37s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:07:19 | 200 |          5m1s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:07:29 | 200 |         4m55s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:07:38 | 200 |         4m51s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:08:59 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-23T11:08:59.546Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/23 - 11:09:13 | 200 |         5m32s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-23T11:09:22.845Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/23 - 11:09:22 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:09:49 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:10:06 | 200 |         6m24s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:10:44 | 200 |         5m55s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:11:42 | 200 |         4m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:12:35 | 200 |         5m15s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:12:48 | 200 |         3m35s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:14:33 | 200 |         4m50s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:16:30 | 200 |         6m23s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:17:22 | 200 |         6m38s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-23T11:17:35.063Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/23 - 11:17:35 | 500 |         4m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-23T11:17:48.656Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/23 - 11:17:48 | 500 |         4m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:17:52 | 200 |         8m48s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:18:29 | 200 |          9m1s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/23 - 11:18:52 | 200 |         8m57s |   192.168.0.131 | POST     "/api/chat"


Sample HTTP response

[
  {
    "model": "phi4:14b",
    "created_at": "2025-12-23T10:42:11.081185972Z",
    "message": {
      "role": "assistant",
      "content": "{<Removed content>}"
    },
    "done": true,
    "done_reason": "stop",
    "total_duration": 148430387965,
    "load_duration": 70605942,
    "prompt_eval_count": 436,
    "prompt_eval_duration": 673136687,
    "eval_count": 685,
    "eval_duration": 79242104555
  }
]

[
  {
    "model": "gemma3:12b",
    "created_at": "2025-12-23T11:20:33.398751558Z",
    "message": {
      "role": "assistant",
      "content": "{<Removed content>}"
    },
    "done": true,
    "done_reason": "stop",
    "total_duration": 63205091351,
    "load_duration": 251629470,
    "prompt_eval_count": 803,
    "prompt_eval_duration": 975409065,
    "eval_count": 690,
    "eval_duration": 61179132852
  }
]

OS

Linux

GPU

Nvidia

CPU

Other

Ollama version

0.13.3

Originally created by @Bcwz on GitHub (Dec 23, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/13552 ### What is the issue? I'm using the following models and Ollama is taking quite some time to response to a simple http /api/chat request. I'm using 2x Nvidia DGX Spark. Running Ollama using Docker (docker run -d --gpus=all -v "$(pwd)/app-data:/root/.ollama" -p 11434:11434 --name local-ollama-gpu ollama/ollama) 1) gpt-oss:20b 2) phi4:14b 3) gemma3:12b 4) qwen3:8b 5) granite3.3:8b Tried reinstalling Ollama, redownloading the models, restarting the system and still same issue. It used to be running a bit faster, but after awhile (20~30mins) it starts to become very slow to response. I am expecting Ollama to response much faster given the hardware and small model size. Any ways to make the models response under a min instead of > 10mins per request? ### Relevant log output ```shell docker exec -it local-ollama-gpu nvidia-smi Tue Dec 23 11:13:46 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GB10 On | 0000000F:01:00.0 On | N/A | | N/A 78C P0 47W / N/A | Not Supported | 92% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 248 C /usr/bin/ollama 8909MiB | | 0 N/A N/A 295 C /usr/bin/ollama 12678MiB | +-----------------------------------------------------------------------------------------+ time=2025-12-23T10:26:21.168Z level=INFO source=routes.go:1544 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GGML_VK_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_REMOTES:[ollama.com] OLLAMA_SCHED_SPREAD:false OLLAMA_VULKAN:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]" time=2025-12-23T10:26:21.168Z level=INFO source=images.go:522 msg="total blobs: 0" time=2025-12-23T10:26:21.168Z level=INFO source=images.go:529 msg="total unused blobs removed: 0" time=2025-12-23T10:26:21.168Z level=INFO source=routes.go:1597 msg="Listening on [::]:11434 (version 0.13.2)" time=2025-12-23T10:26:21.169Z level=INFO source=runner.go:67 msg="discovering available GPUs..." time=2025-12-23T10:26:21.169Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 39717" time=2025-12-23T10:26:21.459Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 38293" time=2025-12-23T10:26:21.804Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 42057" time=2025-12-23T10:26:21.804Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 43237" time=2025-12-23T10:26:22.175Z level=INFO source=types.go:42 msg="inference compute" id=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca filter_id="" library=CUDA compute=12.1 name=CUDA0 description="NVIDIA GB10" libdirs=ollama,cuda_v13 driver=13.0 pci_id=000f:01:00.0 type=iGPU total="119.6 GiB" available="112.9 GiB" [GIN] 2025/12/23 - 10:26:44 | 200 | 38.32µs | 127.0.0.1 | HEAD "/" time=2025-12-23T10:26:45.911Z level=INFO source=download.go:177 msg="downloading e7b273f96360 in 16 862 MB part(s)" [GIN] 2025/12/23 - 10:27:21 | 200 | 25.52µs | 127.0.0.1 | HEAD "/" time=2025-12-23T10:27:23.207Z level=INFO source=download.go:177 msg="downloading 77bcee066a76 in 16 308 MB part(s)" [GIN] 2025/12/23 - 10:28:07 | 200 | 34.976µs | 127.0.0.1 | HEAD "/" time=2025-12-23T10:28:09.354Z level=INFO source=download.go:177 msg="downloading e8ad13eff07a in 16 509 MB part(s)" [GIN] 2025/12/23 - 10:28:26 | 200 | 27.92µs | 127.0.0.1 | HEAD "/" time=2025-12-23T10:28:27.895Z level=INFO source=download.go:177 msg="downloading a3de86cd1c13 in 16 326 MB part(s)" [GIN] 2025/12/23 - 10:28:53 | 200 | 28.672µs | 127.0.0.1 | HEAD "/" time=2025-12-23T10:28:55.215Z level=INFO source=download.go:177 msg="downloading fd7b6731c33c in 16 565 MB part(s)" time=2025-12-23T10:31:05.552Z level=INFO source=download.go:177 msg="downloading 3da071a01bbe in 1 6.6 KB part(s)" time=2025-12-23T10:31:07.400Z level=INFO source=download.go:177 msg="downloading 4a99a6dd617d in 1 11 KB part(s)" time=2025-12-23T10:31:09.320Z level=INFO source=download.go:177 msg="downloading 122661774644 in 1 417 B part(s)" [GIN] 2025/12/23 - 10:31:14 | 200 | 3m52s | 127.0.0.1 | POST "/api/pull" time=2025-12-23T10:31:57.577Z level=INFO source=download.go:177 msg="downloading ae370d884f10 in 1 1.7 KB part(s)" time=2025-12-23T10:31:59.365Z level=INFO source=download.go:177 msg="downloading d18a5cc71b84 in 1 11 KB part(s)" time=2025-12-23T10:32:01.026Z level=INFO source=download.go:177 msg="downloading cff3f395ef37 in 1 120 B part(s)" time=2025-12-23T10:32:02.664Z level=INFO source=download.go:177 msg="downloading 05a61d37b084 in 1 487 B part(s)" [GIN] 2025/12/23 - 10:32:06 | 200 | 3m40s | 127.0.0.1 | POST "/api/pull" time=2025-12-23T10:32:27.601Z level=INFO source=download.go:177 msg="downloading fa6710a93d78 in 1 7.2 KB part(s)" time=2025-12-23T10:32:29.248Z level=INFO source=download.go:177 msg="downloading f60356777647 in 1 11 KB part(s)" time=2025-12-23T10:32:30.926Z level=INFO source=download.go:177 msg="downloading d8ba2f9a17b3 in 1 18 B part(s)" time=2025-12-23T10:32:32.161Z level=INFO source=download.go:177 msg="downloading e0a42594d802 in 1 358 B part(s)" time=2025-12-23T10:32:32.548Z level=INFO source=download.go:177 msg="downloading 776beb3adb23 in 1 489 B part(s)" time=2025-12-23T10:32:33.768Z level=INFO source=download.go:177 msg="downloading dd084c7d92a3 in 1 8.4 KB part(s)" time=2025-12-23T10:32:35.351Z level=INFO source=download.go:177 msg="downloading 3116c5225075 in 1 77 B part(s)" time=2025-12-23T10:32:36.787Z level=INFO source=download.go:177 msg="downloading 32695b892af8 in 1 275 B part(s)" time=2025-12-23T10:32:36.951Z level=INFO source=download.go:177 msg="downloading 6819964c2bcf in 1 490 B part(s)" time=2025-12-23T10:32:38.368Z level=INFO source=download.go:177 msg="downloading fa8235e5b48f in 1 1.1 KB part(s)" time=2025-12-23T10:32:39.929Z level=INFO source=download.go:177 msg="downloading 45a1c652dddc in 1 82 B part(s)" [GIN] 2025/12/23 - 10:32:41 | 200 | 5m56s | 127.0.0.1 | POST "/api/pull" time=2025-12-23T10:32:41.503Z level=INFO source=download.go:177 msg="downloading f5d6f49c6477 in 1 486 B part(s)" [GIN] 2025/12/23 - 10:32:42 | 200 | 4m34s | 127.0.0.1 | POST "/api/pull" [GIN] 2025/12/23 - 10:32:47 | 200 | 3m53s | 127.0.0.1 | POST "/api/pull" time=2025-12-23T10:33:42.676Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 35353" time=2025-12-23T10:33:42.967Z level=WARN source=cpu_linux.go:130 msg="failed to parse CPU allowed micro secs" error="strconv.ParseInt: parsing \"max\": invalid syntax" llama_model_loader: loaded meta data with 33 key-value pairs and 243 tensors from /root/.ollama/models/blobs/sha256-fd7b6731c33c57f61767612f56517460ec2d1e2e5a3f0163e0eb3d8d8cb5df20 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = phi3 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Phi 4 llama_model_loader: - kv 3: general.version str = 4 llama_model_loader: - kv 4: general.organization str = Microsoft llama_model_loader: - kv 5: general.basename str = phi llama_model_loader: - kv 6: general.size_label str = 15B llama_model_loader: - kv 7: general.license str = mit llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/microsoft/phi-... llama_model_loader: - kv 9: general.tags arr[str,7] = ["phi", "nlp", "math", "code", "chat"... llama_model_loader: - kv 10: general.languages arr[str,1] = ["en"] llama_model_loader: - kv 11: phi3.context_length u32 = 16384 llama_model_loader: - kv 12: phi3.rope.scaling.original_context_length u32 = 16384 llama_model_loader: - kv 13: phi3.embedding_length u32 = 5120 llama_model_loader: - kv 14: phi3.feed_forward_length u32 = 17920 llama_model_loader: - kv 15: phi3.block_count u32 = 40 llama_model_loader: - kv 16: phi3.attention.head_count u32 = 40 llama_model_loader: - kv 17: phi3.attention.head_count_kv u32 = 10 llama_model_loader: - kv 18: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 19: phi3.rope.dimension_count u32 = 128 llama_model_loader: - kv 20: phi3.rope.freq_base f32 = 250000.000000 llama_model_loader: - kv 21: general.file_type u32 = 15 llama_model_loader: - kv 22: phi3.attention.sliding_window u32 = 131072 llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 24: tokenizer.ggml.pre str = dbrx llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,100352] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,100352] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,100000] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 28: tokenizer.ggml.bos_token_id u32 = 100257 llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 100257 llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 100257 llama_model_loader: - kv 31: tokenizer.chat_template str = {% for message in messages %}{% if (m... llama_model_loader: - kv 32: general.quantization_version u32 = 2 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q4_K: 101 tensors llama_model_loader: - type q5_K: 40 tensors llama_model_loader: - type q6_K: 21 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 8.43 GiB (4.94 BPW) load: printing all EOG tokens: load: - 100257 ('<|endoftext|>') load: - 100265 ('<|im_end|>') load: special tokens cache size = 96 load: token to piece cache size = 0.6151 MB print_info: arch = phi3 print_info: vocab_only = 1 print_info: model type = ?B print_info: model params = 14.66 B print_info: general.name = Phi 4 print_info: vocab type = BPE print_info: n_vocab = 100352 print_info: n_merges = 100000 print_info: BOS token = 100257 '<|endoftext|>' print_info: EOS token = 100257 '<|endoftext|>' print_info: EOT token = 100265 '<|im_end|>' print_info: PAD token = 100257 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 100258 '<|fim_prefix|>' print_info: FIM SUF token = 100260 '<|fim_suffix|>' print_info: FIM MID token = 100259 '<|fim_middle|>' print_info: EOG token = 100257 '<|endoftext|>' print_info: EOG token = 100265 '<|im_end|>' print_info: max token length = 256 llama_model_load: vocab only - skipping tensors time=2025-12-23T10:33:43.060Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --model /root/.ollama/models/blobs/sha256-fd7b6731c33c57f61767612f56517460ec2d1e2e5a3f0163e0eb3d8d8cb5df20 --port 37983" time=2025-12-23T10:33:43.060Z level=INFO source=sched.go:443 msg="system memory" total="119.6 GiB" free="80.0 GiB" free_swap="16.0 GiB" time=2025-12-23T10:33:43.060Z level=INFO source=sched.go:450 msg="gpu memory" id=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca library=CUDA available="112.2 GiB" free="112.6 GiB" minimum="457.0 MiB" overhead="0 B" time=2025-12-23T10:33:43.060Z level=INFO source=server.go:459 msg="loading model" "model layers"=41 requested=-1 time=2025-12-23T10:33:43.061Z level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="8.2 GiB" time=2025-12-23T10:33:43.061Z level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="800.0 MiB" time=2025-12-23T10:33:43.061Z level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="533.3 MiB" time=2025-12-23T10:33:43.061Z level=INFO source=device.go:272 msg="total memory" size="9.5 GiB" time=2025-12-23T10:33:43.067Z level=INFO source=runner.go:963 msg="starting go runner" load_backend: loaded CPU backend from /usr/lib/ollama/libggml-cpu.so ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, ID: GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca load_backend: loaded CUDA backend from /usr/lib/ollama/cuda_v13/libggml-cuda.so time=2025-12-23T10:33:43.273Z level=INFO source=ggml.go:104 msg=system CPU.0.NEON=1 CPU.0.ARM_FMA=1 CPU.0.LLAMAFILE=1 CPU.1.NEON=1 CPU.1.ARM_FMA=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang) time=2025-12-23T10:33:43.273Z level=INFO source=runner.go:999 msg="Server listening on 127.0.0.1:37983" time=2025-12-23T10:33:43.276Z level=INFO source=runner.go:893 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:false KvSize:4096 KvCacheType: NumThreads:20 GPULayers:41[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:41(0..40)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:true}" time=2025-12-23T10:33:43.277Z level=INFO source=server.go:1294 msg="waiting for llama runner to start responding" time=2025-12-23T10:33:43.277Z level=INFO source=server.go:1328 msg="waiting for server to become available" status="llm server loading model" ggml_nvml_get_device_memory NVML not supported for memory query, using system memory (total=128457203712, available=120889769984) ggml_backend_cuda_device_get_memory device GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca utilizing NVML memory reporting free: 120889769984 total: 128457203712 llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 115289 MiB free llama_model_loader: loaded meta data with 33 key-value pairs and 243 tensors from /root/.ollama/models/blobs/sha256-fd7b6731c33c57f61767612f56517460ec2d1e2e5a3f0163e0eb3d8d8cb5df20 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = phi3 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Phi 4 llama_model_loader: - kv 3: general.version str = 4 llama_model_loader: - kv 4: general.organization str = Microsoft llama_model_loader: - kv 5: general.basename str = phi llama_model_loader: - kv 6: general.size_label str = 15B llama_model_loader: - kv 7: general.license str = mit llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/microsoft/phi-... llama_model_loader: - kv 9: general.tags arr[str,7] = ["phi", "nlp", "math", "code", "chat"... llama_model_loader: - kv 10: general.languages arr[str,1] = ["en"] llama_model_loader: - kv 11: phi3.context_length u32 = 16384 llama_model_loader: - kv 12: phi3.rope.scaling.original_context_length u32 = 16384 llama_model_loader: - kv 13: phi3.embedding_length u32 = 5120 llama_model_loader: - kv 14: phi3.feed_forward_length u32 = 17920 llama_model_loader: - kv 15: phi3.block_count u32 = 40 llama_model_loader: - kv 16: phi3.attention.head_count u32 = 40 llama_model_loader: - kv 17: phi3.attention.head_count_kv u32 = 10 llama_model_loader: - kv 18: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 19: phi3.rope.dimension_count u32 = 128 llama_model_loader: - kv 20: phi3.rope.freq_base f32 = 250000.000000 llama_model_loader: - kv 21: general.file_type u32 = 15 llama_model_loader: - kv 22: phi3.attention.sliding_window u32 = 131072 llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 24: tokenizer.ggml.pre str = dbrx llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,100352] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,100352] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,100000] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 28: tokenizer.ggml.bos_token_id u32 = 100257 llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 100257 llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 100257 llama_model_loader: - kv 31: tokenizer.chat_template str = {% for message in messages %}{% if (m... llama_model_loader: - kv 32: general.quantization_version u32 = 2 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q4_K: 101 tensors llama_model_loader: - type q5_K: 40 tensors llama_model_loader: - type q6_K: 21 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 8.43 GiB (4.94 BPW) load_hparams: Phi SWA is currently disabled - results might be suboptimal for some models (see https://github.com/ggml-org/llama.cpp/pull/13676) load: printing all EOG tokens: load: - 100257 ('<|endoftext|>') load: - 100265 ('<|im_end|>') load: special tokens cache size = 96 load: token to piece cache size = 0.6151 MB print_info: arch = phi3 print_info: vocab_only = 0 print_info: n_ctx_train = 16384 print_info: n_embd = 5120 print_info: n_embd_inp = 5120 print_info: n_layer = 40 print_info: n_head = 40 print_info: n_head_kv = 10 print_info: n_rot = 128 print_info: n_swa = 0 print_info: is_swa_any = 0 print_info: n_embd_head_k = 128 print_info: n_embd_head_v = 128 print_info: n_gqa = 4 print_info: n_embd_k_gqa = 1280 print_info: n_embd_v_gqa = 1280 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-05 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: f_attn_scale = 0.0e+00 print_info: n_ff = 17920 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: n_expert_groups = 0 print_info: n_group_used = 0 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 2 print_info: rope scaling = linear print_info: freq_base_train = 250000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 16384 print_info: rope_finetuned = unknown print_info: model type = 14B print_info: model params = 14.66 B print_info: general.name = Phi 4 print_info: vocab type = BPE print_info: n_vocab = 100352 print_info: n_merges = 100000 print_info: BOS token = 100257 '<|endoftext|>' print_info: EOS token = 100257 '<|endoftext|>' print_info: EOT token = 100265 '<|im_end|>' print_info: PAD token = 100257 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 100258 '<|fim_prefix|>' print_info: FIM SUF token = 100260 '<|fim_suffix|>' print_info: FIM MID token = 100259 '<|fim_middle|>' print_info: EOG token = 100257 '<|endoftext|>' print_info: EOG token = 100265 '<|im_end|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: offloading 40 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 41/41 layers to GPU load_tensors: CPU_Mapped model buffer size = 275.62 MiB load_tensors: CUDA0 model buffer size = 8354.71 MiB llama_context: constructing llama_context llama_context: n_seq_max = 1 llama_context: n_ctx = 4096 llama_context: n_ctx_seq = 4096 llama_context: n_batch = 512 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = disabled llama_context: kv_unified = false llama_context: freq_base = 250000.0 llama_context: freq_scale = 1 llama_context: n_ctx_seq (4096) < n_ctx_train (16384) -- the full capacity of the model will not be utilized llama_context: CUDA_Host output buffer size = 0.40 MiB llama_kv_cache: CUDA0 KV buffer size = 800.00 MiB llama_kv_cache: size = 800.00 MiB ( 4096 cells, 40 layers, 1/1 seqs), K (f16): 400.00 MiB, V (f16): 400.00 MiB llama_context: CUDA0 compute buffer size = 353.01 MiB llama_context: CUDA_Host compute buffer size = 23.01 MiB llama_context: graph nodes = 1366 llama_context: graph splits = 2 time=2025-12-23T10:34:16.929Z level=INFO source=server.go:1332 msg="llama runner started in 33.87 seconds" time=2025-12-23T10:34:16.929Z level=INFO source=sched.go:517 msg="loaded runners" count=1 time=2025-12-23T10:34:16.929Z level=INFO source=server.go:1294 msg="waiting for llama runner to start responding" time=2025-12-23T10:34:16.929Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --port 46307" time=2025-12-23T10:34:16.930Z level=INFO source=server.go:1332 msg="llama runner started in 33.87 seconds" time=2025-12-23T10:34:17.388Z level=WARN source=cpu_linux.go:130 msg="failed to parse CPU allowed micro secs" error="strconv.ParseInt: parsing \"max\": invalid syntax" time=2025-12-23T10:34:17.404Z level=INFO source=sched.go:583 msg="updated VRAM based on existing loaded models" gpu=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca library=CUDA total="119.6 GiB" available="102.1 GiB" time=2025-12-23T10:34:17.457Z level=INFO source=server.go:209 msg="enabling flash attention" time=2025-12-23T10:34:17.458Z level=INFO source=server.go:392 msg="starting runner" cmd="/usr/bin/ollama runner --ollama-engine --model /root/.ollama/models/blobs/sha256-a3de86cd1c132c822487ededd47a324c50491393e6565cd14bafa40d0b8e686f --port 35313" time=2025-12-23T10:34:17.458Z level=INFO source=sched.go:443 msg="system memory" total="119.6 GiB" free="79.0 GiB" free_swap="16.0 GiB" time=2025-12-23T10:34:17.458Z level=INFO source=sched.go:450 msg="gpu memory" id=GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca library=CUDA available="101.7 GiB" free="102.1 GiB" minimum="457.0 MiB" overhead="0 B" time=2025-12-23T10:34:17.458Z level=INFO source=server.go:702 msg="loading model" "model layers"=37 requested=-1 time=2025-12-23T10:34:17.472Z level=INFO source=runner.go:1398 msg="starting ollama engine" time=2025-12-23T10:34:17.472Z level=INFO source=runner.go:1433 msg="Server listening on 127.0.0.1:35313" time=2025-12-23T10:34:17.480Z level=INFO source=runner.go:1271 msg=load request="{Operation:fit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:true KvSize:4096 KvCacheType: NumThreads:20 GPULayers:37[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:37(0..36)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}" time=2025-12-23T10:34:17.505Z level=INFO source=ggml.go:136 msg="" architecture=qwen3 file_type=Q4_K_M name="Qwen3 8B" description="" num_tensors=399 num_key_values=29 load_backend: loaded CPU backend from /usr/lib/ollama/libggml-cpu.so ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, ID: GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca load_backend: loaded CUDA backend from /usr/lib/ollama/cuda_v13/libggml-cuda.so time=2025-12-23T10:34:18.078Z level=INFO source=ggml.go:104 msg=system CPU.0.NEON=1 CPU.0.ARM_FMA=1 CPU.0.LLAMAFILE=1 CPU.1.NEON=1 CPU.1.ARM_FMA=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang) time=2025-12-23T10:34:24.803Z level=INFO source=runner.go:1271 msg=load request="{Operation:alloc LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:true KvSize:4096 KvCacheType: NumThreads:20 GPULayers:37[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:37(0..36)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}" time=2025-12-23T10:34:25.322Z level=INFO source=runner.go:1271 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:true KvSize:4096 KvCacheType: NumThreads:20 GPULayers:37[ID:GPU-7c001c9e-a080-9ccb-068e-b18ea57d5cca Layers:37(0..36)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}" time=2025-12-23T10:34:25.322Z level=INFO source=ggml.go:482 msg="offloading 36 repeating layers to GPU" time=2025-12-23T10:34:25.322Z level=INFO source=ggml.go:489 msg="offloading output layer to GPU" time=2025-12-23T10:34:25.322Z level=INFO source=ggml.go:494 msg="offloaded 37/37 layers to GPU" time=2025-12-23T10:34:25.322Z level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="4.5 GiB" time=2025-12-23T10:34:25.322Z level=INFO source=device.go:245 msg="model weights" device=CPU size="333.8 MiB" time=2025-12-23T10:34:25.322Z level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="576.0 MiB" time=2025-12-23T10:34:25.322Z level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="126.0 MiB" time=2025-12-23T10:34:25.322Z level=INFO source=device.go:267 msg="compute graph" device=CPU size="8.0 MiB" time=2025-12-23T10:34:25.322Z level=INFO source=device.go:272 msg="total memory" size="5.6 GiB" time=2025-12-23T10:34:25.323Z level=INFO source=sched.go:517 msg="loaded runners" count=2 time=2025-12-23T10:34:25.323Z level=INFO source=server.go:1294 msg="waiting for llama runner to start responding" time=2025-12-23T10:34:25.323Z level=INFO source=server.go:1328 msg="waiting for server to become available" status="llm server loading model" time=2025-12-23T10:34:26.076Z level=INFO source=server.go:1332 msg="llama runner started in 8.62 seconds" [GIN] 2025/12/23 - 10:34:40 | 200 | 57.956598944s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:36:24 | 200 | 1m43s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:37:18 | 200 | 3m24s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:37:20 | 200 | 56.430880799s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:37:36 | 200 | 3m21s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:38:33 | 200 | 1m50s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:38:44 | 200 | 1m23s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:40:36 | 200 | 3m18s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:40:50 | 200 | 2m17s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:40:57 | 200 | 3m21s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:42:11 | 200 | 2m28s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:42:28 | 200 | 1m37s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:45:17 | 200 | 4m41s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:45:20 | 200 | 4m22s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:45:31 | 200 | 3m19s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:45:41 | 200 | 3m12s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:47:01 | 200 | 4m18s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:47:36 | 200 | 2m5s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:48:09 | 200 | 2m27s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:48:31 | 200 | 3m13s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:48:34 | 200 | 3m13s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:50:19 | 200 | 3m18s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:50:24 | 200 | 2m48s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:51:13 | 200 | 3m3s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:52:00 | 200 | 3m18s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:52:24 | 200 | 2m4s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:52:44 | 200 | 1m31s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:53:46 | 200 | 2m4s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:53:59 | 200 | 3m9s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:54:22 | 200 | 1m33s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:54:34 | 200 | 2m28s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:54:44 | 200 | 2m20s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:54:53 | 200 | 2m9s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:55:40 | 200 | 1m48s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:56:14 | 200 | 1m35s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:57:21 | 200 | 2m38s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:58:33 | 200 | 2m53s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 10:59:36 | 200 | 3m21s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:01:01 | 200 | 3m35s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:02:18 | 200 | 4m35s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:02:34 | 200 | 4m0s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:02:46 | 200 | 3m10s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:03:40 | 200 | 2m58s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:04:43 | 200 | 3m37s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:07:19 | 200 | 5m1s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:07:29 | 200 | 4m55s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:07:38 | 200 | 4m51s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:08:59 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-23T11:08:59.546Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/23 - 11:09:13 | 200 | 5m32s | 192.168.0.131 | POST "/api/chat" time=2025-12-23T11:09:22.845Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/23 - 11:09:22 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:09:49 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:10:06 | 200 | 6m24s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:10:44 | 200 | 5m55s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:11:42 | 200 | 4m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:12:35 | 200 | 5m15s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:12:48 | 200 | 3m35s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:14:33 | 200 | 4m50s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:16:30 | 200 | 6m23s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:17:22 | 200 | 6m38s | 192.168.0.131 | POST "/api/chat" time=2025-12-23T11:17:35.063Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/23 - 11:17:35 | 500 | 4m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-23T11:17:48.656Z level=INFO source=server.go:1497 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/23 - 11:17:48 | 500 | 4m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:17:52 | 200 | 8m48s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:18:29 | 200 | 9m1s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/23 - 11:18:52 | 200 | 8m57s | 192.168.0.131 | POST "/api/chat" Sample HTTP response [ { "model": "phi4:14b", "created_at": "2025-12-23T10:42:11.081185972Z", "message": { "role": "assistant", "content": "{<Removed content>}" }, "done": true, "done_reason": "stop", "total_duration": 148430387965, "load_duration": 70605942, "prompt_eval_count": 436, "prompt_eval_duration": 673136687, "eval_count": 685, "eval_duration": 79242104555 } ] [ { "model": "gemma3:12b", "created_at": "2025-12-23T11:20:33.398751558Z", "message": { "role": "assistant", "content": "{<Removed content>}" }, "done": true, "done_reason": "stop", "total_duration": 63205091351, "load_duration": 251629470, "prompt_eval_count": 803, "prompt_eval_duration": 975409065, "eval_count": 690, "eval_duration": 61179132852 } ] ``` ### OS Linux ### GPU Nvidia ### CPU Other ### Ollama version 0.13.3
GiteaMirror added the bug label 2026-04-22 18:26:26 -05:00
GiteaMirror commented 2026-04-22 18:26:28 -05:00
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@rick-github commented on GitHub (Dec 23, 2025):

What client? How long are the prompts? Does the client concatenate the response with the previous messages when sending a new prompt? Does increasing the context window change the behaviour? Have you tried increasing parallelism?

<!-- gh-comment-id:3687333093 --> @rick-github commented on GitHub (Dec 23, 2025): What client? How long are the prompts? Does the client concatenate the response with the previous messages when sending a new prompt? Does increasing the [context window](https://docs.ollama.com/context-length#context-length) change the behaviour? Have you tried [increasing parallelism](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests)?
GiteaMirror commented 2026-04-22 18:26:29 -05:00
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@Bcwz commented on GitHub (Dec 24, 2025):

What client?

  • Currently using n8n to send a http POST request every ~2/3mins.

How long are the prompts?

  • Under/around 1k.

Does the client concatenate the response with the previous messages when sending a new prompt?

  • No, it only sends the current payload.

Does increasing the context window change the behaviour? Have you tried increasing parallelism?

I've added -e OLLAMA_CONTEXT_LENGTH=32000 -e OLLAMA_NUM_PARALLEL=10 when starting the docker container.
I'm using the same prompt for all models

Response time for phi4
[GIN] 2025/12/24 - 08:35:48 | 200 |         7m31s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:36:30 | 200 |         1m54s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:36:30 | 200 |         2m42s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:37:06 | 200 |         4m20s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:37:36 | 200 |         1m47s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:37:51 | 200 |         1m45s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:37:51 | 200 |         2m28s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:38:20 | 200 |         1m50s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:38:20 | 200 | 30.379215053s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:38:21 | 200 |         6m13s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:38:54 | 200 |         5m13s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:17 | 200 |         1m26s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:29 | 200 |         8m52s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:32 | 200 |         4m11s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:36 | 200 |         6m11s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:57 | 200 | 28.611843842s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:59 | 200 |         2m53s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:40:28 | 200 |         2m22s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:40:28 | 200 | 30.990508131s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:40:41 | 200 |          1m4s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:40:42 | 200 |         4m35s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:40:46 | 200 |         1m28s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:41:28 | 200 |  47.01973282s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:41:47 | 200 |         3m56s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:41:52 | 200 |         2m53s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:41:54 | 200 |         1m55s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:42:01 | 200 |         1m56s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:42:16 | 200 |          2m9s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:42:21 | 200 |         11m5s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:43:22 | 200 |         1m34s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:43:33 | 200 |         1m27s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:01 | 200 |         3m58s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:26 | 200 |          1m3s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:37 | 200 |          4m8s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:54 | 200 |         2m37s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:54 | 200 |         2m52s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:59 | 200 |          3m1s |   192.168.0.131 | POST     "/api/chat"



Response time for gemma3
[GIN] 2025/12/24 - 08:35:36 | 200 |         1m17s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:35:47 | 200 | 13.529008829s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:36:31 | 200 |         4m19s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:36:33 | 200 |         2m45s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:37:05 | 200 |         6m45s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:38:59 | 500 |         4m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:44 | 200 |         2m38s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:39:48 | 200 |         1m29s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:40:44 | 200 |        11m12s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:41:53 | 200 |          1m8s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:43:14 | 200 |         4m53s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:43:32 | 200 |         3m44s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:01 | 200 |         1m41s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:04 | 500 |         4m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:10 | 200 | 37.523910639s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:11 | 200 |         1m52s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:44:38 | 200 | 14.178038681s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:45:07 | 200 |        10m46s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:47:03 | 200 |         3m49s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:47:16 | 200 | 56.555601016s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:47:31 | 200 |          3m6s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:48:10 | 200 | 37.911952614s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:48:33 | 200 |         2m14s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:48:38 | 200 | 14.586498134s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:48:52 | 200 |         4m51s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:49:05 | 200 |         9m21s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:49:19 | 200 |         4m12s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:49:54 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:50:08 | 200 |         2m51s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:50:36 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:51:15 | 200 | 57.273125299s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/24 - 08:52:54 | 200 |         2m13s |   192.168.0.131 | POST     "/api/chat"



Sample response from qwen3 and phi4
[
  {
    "model": "qwen3:8b",
    "created_at": "2025-12-24T08:28:24.693589245Z",
    "message": {
      "role": "assistant",
      "content": "{<Removed Content>}",
      "thinking": "<Removed Thinking>"
    },
    "done": true,
    "done_reason": "stop",
    "total_duration": 228964677714,
    "load_duration": 108401361,
    "prompt_eval_count": 2027,
    "prompt_eval_duration": 3060126313,
    "eval_count": 532,
    "eval_duration": 63497843977
  }
]

[
  {
    "model": "phi4:14b",
    "created_at": "2025-12-24T08:35:48.901384619Z",
    "message": {
      "role": "assistant",
      "content": "{<Removed content>}"
    },
    "done": true,
    "done_reason": "stop",
    "total_duration": 451938110052,
    "load_duration": 71218989,
    "prompt_eval_count": 936,
    "prompt_eval_duration": 2556995980,
    "eval_count": 1298,
    "eval_duration": 445977673299
  }
]

docker exec -it local-ollama-gpu ollama ps
NAME           ID              SIZE     PROCESSOR    CONTEXT    UNTIL            
phi4:14b       ac896e5b8b34    64 GB    100% GPU     32000      4 hours from now    
gpt-oss:20b    17052f91a42e    23 GB    100% GPU     32000      4 hours from now   
qwen3:8b      500a1f067a9f    53 GB    100% GPU     32000      6 hours from now    
gemma3:12b    f4031aab637d    34 GB    100% GPU     32000      6 hours from now

After adding the -e OLLAMA_CONTEXT_LENGTH=32000 -e OLLAMA_NUM_PARALLEL=10 parameter, the slower response time seems to be around 5mins~10mins per requests. There are still occasionally 15mins response time, resulting in http 500 response.

*Edit
After running for a few hours, it starts to timeout pretty consistently

Sample logs
time=2025-12-25T03:11:47.436Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:11:47 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/25 - 03:12:36 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:12:36.740Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:12:43 | 500 |         15m1s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:12:43.657Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
time=2025-12-25T03:12:44.093Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:12:44 | 500 |         15m2s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:12:48.719Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:12:48 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:12:52.510Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:12:52 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/25 - 03:13:00 | 200 |         1m40s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/25 - 03:13:24 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:13:24.690Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
time=2025-12-25T03:13:28.529Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:13:28 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/25 - 03:13:47 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:13:47.499Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:14:10 | 500 |         15m0s |   192.168.0.131 | POST     "/api/chat"
[GIN] 2025/12/25 - 03:14:24 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:14:24.167Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
[GIN] 2025/12/25 - 03:14:42 | 500 |        14m59s |   192.168.0.131 | POST     "/api/chat"
time=2025-12-25T03:14:42.495Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection"
<!-- gh-comment-id:3689136247 --> @Bcwz commented on GitHub (Dec 24, 2025): What client? - Currently using n8n to send a http POST request every ~2/3mins. How long are the prompts? - Under/around 1k. Does the client concatenate the response with the previous messages when sending a new prompt? - No, it only sends the current payload. Does increasing the [context window](https://docs.ollama.com/context-length#context-length) change the behaviour? Have you tried [increasing parallelism](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests)? I've added -e OLLAMA_CONTEXT_LENGTH=32000 -e OLLAMA_NUM_PARALLEL=10 when starting the docker container. I'm using the same prompt for all models ``` Response time for phi4 [GIN] 2025/12/24 - 08:35:48 | 200 | 7m31s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:36:30 | 200 | 1m54s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:36:30 | 200 | 2m42s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:37:06 | 200 | 4m20s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:37:36 | 200 | 1m47s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:37:51 | 200 | 1m45s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:37:51 | 200 | 2m28s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:38:20 | 200 | 1m50s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:38:20 | 200 | 30.379215053s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:38:21 | 200 | 6m13s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:38:54 | 200 | 5m13s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:17 | 200 | 1m26s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:29 | 200 | 8m52s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:32 | 200 | 4m11s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:36 | 200 | 6m11s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:57 | 200 | 28.611843842s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:59 | 200 | 2m53s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:40:28 | 200 | 2m22s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:40:28 | 200 | 30.990508131s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:40:41 | 200 | 1m4s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:40:42 | 200 | 4m35s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:40:46 | 200 | 1m28s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:41:28 | 200 | 47.01973282s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:41:47 | 200 | 3m56s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:41:52 | 200 | 2m53s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:41:54 | 200 | 1m55s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:42:01 | 200 | 1m56s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:42:16 | 200 | 2m9s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:42:21 | 200 | 11m5s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:43:22 | 200 | 1m34s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:43:33 | 200 | 1m27s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:01 | 200 | 3m58s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:26 | 200 | 1m3s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:37 | 200 | 4m8s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:54 | 200 | 2m37s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:54 | 200 | 2m52s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:59 | 200 | 3m1s | 192.168.0.131 | POST "/api/chat" ``` ``` Response time for gemma3 [GIN] 2025/12/24 - 08:35:36 | 200 | 1m17s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:35:47 | 200 | 13.529008829s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:36:31 | 200 | 4m19s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:36:33 | 200 | 2m45s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:37:05 | 200 | 6m45s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:38:59 | 500 | 4m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:44 | 200 | 2m38s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:39:48 | 200 | 1m29s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:40:44 | 200 | 11m12s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:41:53 | 200 | 1m8s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:43:14 | 200 | 4m53s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:43:32 | 200 | 3m44s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:01 | 200 | 1m41s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:04 | 500 | 4m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:10 | 200 | 37.523910639s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:11 | 200 | 1m52s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:44:38 | 200 | 14.178038681s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:45:07 | 200 | 10m46s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:47:03 | 200 | 3m49s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:47:16 | 200 | 56.555601016s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:47:31 | 200 | 3m6s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:48:10 | 200 | 37.911952614s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:48:33 | 200 | 2m14s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:48:38 | 200 | 14.586498134s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:48:52 | 200 | 4m51s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:49:05 | 200 | 9m21s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:49:19 | 200 | 4m12s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:49:54 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:50:08 | 200 | 2m51s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:50:36 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:51:15 | 200 | 57.273125299s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/24 - 08:52:54 | 200 | 2m13s | 192.168.0.131 | POST "/api/chat" ``` ``` Sample response from qwen3 and phi4 [ { "model": "qwen3:8b", "created_at": "2025-12-24T08:28:24.693589245Z", "message": { "role": "assistant", "content": "{<Removed Content>}", "thinking": "<Removed Thinking>" }, "done": true, "done_reason": "stop", "total_duration": 228964677714, "load_duration": 108401361, "prompt_eval_count": 2027, "prompt_eval_duration": 3060126313, "eval_count": 532, "eval_duration": 63497843977 } ] [ { "model": "phi4:14b", "created_at": "2025-12-24T08:35:48.901384619Z", "message": { "role": "assistant", "content": "{<Removed content>}" }, "done": true, "done_reason": "stop", "total_duration": 451938110052, "load_duration": 71218989, "prompt_eval_count": 936, "prompt_eval_duration": 2556995980, "eval_count": 1298, "eval_duration": 445977673299 } ] ``` ``` docker exec -it local-ollama-gpu ollama ps NAME ID SIZE PROCESSOR CONTEXT UNTIL phi4:14b ac896e5b8b34 64 GB 100% GPU 32000 4 hours from now gpt-oss:20b 17052f91a42e 23 GB 100% GPU 32000 4 hours from now qwen3:8b 500a1f067a9f 53 GB 100% GPU 32000 6 hours from now gemma3:12b f4031aab637d 34 GB 100% GPU 32000 6 hours from now ``` After adding the -e OLLAMA_CONTEXT_LENGTH=32000 -e OLLAMA_NUM_PARALLEL=10 parameter, the slower response time seems to be around 5mins~10mins per requests. There are still occasionally 15mins response time, resulting in http 500 response. *Edit After running for a few hours, it starts to timeout pretty consistently ``` Sample logs time=2025-12-25T03:11:47.436Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:11:47 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/25 - 03:12:36 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:12:36.740Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:12:43 | 500 | 15m1s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:12:43.657Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" time=2025-12-25T03:12:44.093Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:12:44 | 500 | 15m2s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:12:48.719Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:12:48 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:12:52.510Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:12:52 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/25 - 03:13:00 | 200 | 1m40s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/25 - 03:13:24 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:13:24.690Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" time=2025-12-25T03:13:28.529Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:13:28 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/25 - 03:13:47 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:13:47.499Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:14:10 | 500 | 15m0s | 192.168.0.131 | POST "/api/chat" [GIN] 2025/12/25 - 03:14:24 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:14:24.167Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" [GIN] 2025/12/25 - 03:14:42 | 500 | 14m59s | 192.168.0.131 | POST "/api/chat" time=2025-12-25T03:14:42.495Z level=INFO source=server.go:1504 msg="aborting completion request due to client closing the connection" ```
GiteaMirror commented 2026-04-22 18:26:31 -05:00
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@seanlinmt commented on GitHub (Dec 28, 2025):

I did a comparison between LM Studio and Ollama serving the same model, hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:bf16, using https://github.com/Yoosu-L/llmapibenchmark

LM Studio is almost 3x faster serving the above model

Image

LM Studio 0.3.36
Ollama 0.13.4

It's almost as if LM Studio is caching responses or there's something wrong with ollama.

<!-- gh-comment-id:3694420691 --> @seanlinmt commented on GitHub (Dec 28, 2025): I did a comparison between LM Studio and Ollama serving the same model, hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:bf16, using https://github.com/Yoosu-L/llmapibenchmark LM Studio is almost 3x faster serving the above model <img width="1106" height="611" alt="Image" src="https://github.com/user-attachments/assets/5e06ca6a-270c-4118-8c77-e00ea4cfd147" /> LM Studio 0.3.36 Ollama 0.13.4 It's almost as if LM Studio is caching responses or there's something wrong with ollama.
GiteaMirror commented 2026-04-22 18:26:31 -05:00
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@rick-github commented on GitHub (Dec 28, 2025):

LMstudio has the advantage that it always uses the latest llama.cpp run-time, while ollama needs a vendor sync to merge it. If you use the most recent version of ollama (0.13.5), the throughput difference isn't quite as large:

$ llmapibenchmark -c 1,10 -u http://ollama-bench:11434/v1 -m hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:bf16 

################################################################################################################
				          LLM API Throughput Benchmark
				    https://github.com/Yoosu-L/llmapibenchmark
					 Time：2025-12-28 06:39:00 UTC+0
################################################################################################################
Input Tokens: 31
Output Tokens: 512
Test Model: hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:bf16
Latency: 0.00 ms

| Concurrency | Generation Throughput (tokens/s) |  Prompt Throughput (tokens/s) | Min TTFT (s) | Max TTFT (s) | Success Rate |
|-------------|----------------------------------|-------------------------------|--------------|--------------|--------------|
|           1 |                            86.17 |                         86.11 |         0.36 |         0.36 |      100.00% |
|          10 |                            88.33 |                          5.95 |         0.68 |        52.12 |      100.00% |

================================================================================================================

$ llmapibenchmark -c 1,10 -u http://lmstudio-bench:1234/v1 -m apertus-8b-instruct-2509

################################################################################################################
				          LLM API Throughput Benchmark
				    https://github.com/Yoosu-L/llmapibenchmark
					 Time：2025-12-28 06:40:55 UTC+0
################################################################################################################
Input Tokens: 87
Output Tokens: 512
Test Model: apertus-8b-instruct-2509
Latency: 0.20 ms

| Concurrency | Generation Throughput (tokens/s) |  Prompt Throughput (tokens/s) | Min TTFT (s) | Max TTFT (s) | Success Rate |
|-------------|----------------------------------|-------------------------------|--------------|--------------|--------------|
|           1 |                            91.86 |                        968.82 |         0.09 |         0.09 |      100.00% |
|          10 |                            91.64 |                         21.18 |         0.09 |        41.07 |      100.00% |

================================================================================================================

LMStudio still has an edge, particularly in the prompt processing. It will interesting to see how the figures compare after the next ollama vendor sync.

<!-- gh-comment-id:3694510842 --> @rick-github commented on GitHub (Dec 28, 2025): LMstudio has the advantage that it always uses the latest llama.cpp run-time, while ollama needs a vendor sync to merge it. If you use the most recent version of ollama (0.13.5), the throughput difference isn't quite as large: ```console $ llmapibenchmark -c 1,10 -u http://ollama-bench:11434/v1 -m hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:bf16 ################################################################################################################ LLM API Throughput Benchmark https://github.com/Yoosu-L/llmapibenchmark Time：2025-12-28 06:39:00 UTC+0 ################################################################################################################ Input Tokens: 31 Output Tokens: 512 Test Model: hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:bf16 Latency: 0.00 ms | Concurrency | Generation Throughput (tokens/s) | Prompt Throughput (tokens/s) | Min TTFT (s) | Max TTFT (s) | Success Rate | |-------------|----------------------------------|-------------------------------|--------------|--------------|--------------| | 1 | 86.17 | 86.11 | 0.36 | 0.36 | 100.00% | | 10 | 88.33 | 5.95 | 0.68 | 52.12 | 100.00% | ================================================================================================================ ``` ```console $ llmapibenchmark -c 1,10 -u http://lmstudio-bench:1234/v1 -m apertus-8b-instruct-2509 ################################################################################################################ LLM API Throughput Benchmark https://github.com/Yoosu-L/llmapibenchmark Time：2025-12-28 06:40:55 UTC+0 ################################################################################################################ Input Tokens: 87 Output Tokens: 512 Test Model: apertus-8b-instruct-2509 Latency: 0.20 ms | Concurrency | Generation Throughput (tokens/s) | Prompt Throughput (tokens/s) | Min TTFT (s) | Max TTFT (s) | Success Rate | |-------------|----------------------------------|-------------------------------|--------------|--------------|--------------| | 1 | 91.86 | 968.82 | 0.09 | 0.09 | 100.00% | | 10 | 91.64 | 21.18 | 0.09 | 41.07 | 100.00% | ================================================================================================================ ``` LMStudio still has an edge, particularly in the prompt processing. It will interesting to see how the figures compare after the next ollama vendor sync.
GiteaMirror commented 2026-04-22 18:26:32 -05:00
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@seanlinmt commented on GitHub (Dec 29, 2025):

@rick-github Thanks for the info. Good to know.

After having a play with both, ollama's advantage is its ability to change model parameters via the API. Lm studio cannot do that, well, that or I haven't found a way. So I cannot automate benchmarking with LM studio.

btw, what hardware are you using? My benchmark was run on a 22GB RTX 2080 TI.

<!-- gh-comment-id:3696078084 --> @seanlinmt commented on GitHub (Dec 29, 2025): @rick-github Thanks for the info. Good to know. After having a play with both, ollama's advantage is its ability to change model parameters via the API. Lm studio cannot do that, well, that or I haven't found a way. So I cannot automate benchmarking with LM studio. btw, what hardware are you using? My benchmark was run on a 22GB RTX 2080 TI.
GiteaMirror commented 2026-04-22 18:26:33 -05:00
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@Bcwz commented on GitHub (Dec 29, 2025):

@seanlinmt , @rick-github,

I would appreciate if you could comment only for the original issue at hand.

<!-- gh-comment-id:3696539046 --> @Bcwz commented on GitHub (Dec 29, 2025): @seanlinmt , @rick-github, I would appreciate if you could comment only for the original issue at hand.
GiteaMirror commented 2026-04-22 18:26:33 -05:00
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@rick-github commented on GitHub (Jan 1, 2026):

Can you share a prompt?

<!-- gh-comment-id:3703418835 --> @rick-github commented on GitHub (Jan 1, 2026): Can you share a prompt?
GiteaMirror commented 2026-04-22 18:26:34 -05:00
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@Bcwz commented on GitHub (Jan 2, 2026):

Can you share a prompt?

This took ~8mins.

{
    "model": "phi4:14b",
    "stream": false,
    "keep_alive": "6h",
    "format": {
        "type": "object",
        "properties": {
            "requirements_brief": {
                "type": "object",
                "properties": {
                    "summary": {
                        "type": "string"
                    },
                    "assumptions": {
                        "type": "array",
                        "items": {
                            "type": "string"
                        }
                    },
                    "non_goals": {
                        "type": "array",
                        "items": {
                            "type": "string"
                        }
                    },
                    "constraints": {
                        "type": "object"
                    }
                },
                "required": [
                    "summary",
                    "assumptions",
                    "non_goals",
                    "constraints"
                ],
                "additionalProperties": false
            },
            "subtasks": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "id": {
                            "type": "string"
                        },
                        "purpose": {
                            "type": "string"
                        },
                        "language": {
                            "type": "string"
                        },
                        "inputs": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "name": {
                                        "type": "string"
                                    },
                                    "type": {
                                        "type": "string"
                                    },
                                    "description": {
                                        "type": "string"
                                    }
                                },
                                "required": [
                                    "name",
                                    "type",
                                    "description"
                                ],
                                "additionalProperties": false
                            }
                        },
                        "outputs": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "type": {
                                        "type": "string"
                                    },
                                    "description": {
                                        "type": "string"
                                    }
                                },
                                "required": [
                                    "type",
                                    "description"
                                ],
                                "additionalProperties": false
                            }
                        },
                        "acceptance_criteria": {
                            "type": "array",
                            "items": {
                                "type": "string"
                            }
                        }
                    },
                    "required": [
                        "id",
                        "purpose",
                        "language",
                        "inputs",
                        "outputs",
                        "acceptance_criteria"
                    ],
                    "additionalProperties": false
                }
            }
        },
        "required": [
            "requirements_brief",
            "subtasks"
        ],
        "additionalProperties": false
    },
    "messages": [
        {
            "role": "system",
            "content": "You are Task Breakdown Agent (AGENT_ID=TASKBREAKDOWN_AGENT_1).\n\nMISSION:\n- Read chatInput from the FIRST element of the input array.\n- Normalize it into a requirements_brief and a list of subtasks.\n\nOUTPUT CONTRACT (STRICT):\n- Respond with exactly one JSON object.\n- Top-level keys must be only \"requirements_brief\" and \"subtasks\".\n- No markdown, no prose, no code fences, no tags, no text outside the JSON.\n- First output character must be '{' and the last must be '}'.\n\nrequirements_brief:\n- Must contain exactly: summary (string), assumptions (string array), non_goals (string array), constraints (object).\n- No extra keys.\n\nsubtasks:\n- An array with at least one element.\n- Each subtask must contain exactly: id, purpose, language, inputs, outputs, acceptance_criteria.\n- id: stable, unique identifiers like \"ST-001\", \"ST-002\", ...\n- language: programming language, default to \"python\" if not specified in chatInput.\n- inputs: array of objects {name, type, description}.\n- outputs: array of objects {type, description}.\n- acceptance_criteria: array of clear, testable statements (Given/When/Then style allowed).\n- No additional keys such as title, agent, dispatch, text, or output.\n\nINPUT FORMAT:\n- You receive a JSON array, for example: [{\"chatInput\": \"...\"}].\n- Use ONLY chatInput from the first element and ignore all other fields.\n\nSTYLE RULES:\n- Do not wrap the output JSON in an extra array or object.\n- Do not include chain-of-thought, explanations, comments, or reasoning.\n- Do not output tokens like <think>, </think>, <analysis>, or similar.\n- Ensure the JSON is syntactically valid with no trailing commas.\n"
        },
        {
            "role": "user",
            "content": "\"\"\"\nWrite a python function to check whether any value in a sequence exists in a sequence or not.\nassert overlapping([1,2,3,4,5],[6,7,8,9]) == False\n\"\"\"\n"
        }
    ]
}

This took ~20mins

{
    "model": "phi4:14b",
    "stream": false,
    "keep_alive": "6h",
    "format": {
        "type": "object",
        "properties": {
            "suggested_score[0][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[0][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[0][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "best_code_id": {
                "type": [
                    "string",
                    "null"
                ]
            },
            "best_score": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            }
        },
        "required": [
            "suggested_score[0][0]",
            "suggested_score[0][1]",
            "suggested_score[0][2]",
            "suggested_score[1][0]",
            "suggested_score[1][1]",
            "suggested_score[1][2]",
            "suggested_score[2][0]",
            "suggested_score[2][1]",
            "suggested_score[2][2]",
            "best_code_id",
            "best_score"
        ],
        "additionalProperties": false
    },
    "messages": [
        {
            "role": "system",
            "content": "You are a JSON scoring engine.\n\nINPUT:\n- One JSON object containing:\n  - \"chatInput\" (natural-language task)\n  - Several \"suggested_code[i][j]\" string fields (candidate implementations).\n\nYOUR JOB:\n- For EVERY suggested_code[i][j], produce one numeric score field\n  named \"suggested_score[i][j]\" in [0.0, 1.0].\n- Then choose exactly ONE best candidate and report it in best_code_id and best_score.\n\nSCORING GUIDELINES (brief):\n- 1.0   : very likely fully correct and robust.\n- 0.7–0.9: mostly correct, minor issues.\n- 0.4–0.6: partially correct.\n- 0.1–0.3: clearly flawed but somewhat relevant.\n- 0.0   : invalid, unrelated, or obviously wrong.\n- Score 0.0 if a candidate has markdown fences, long prose, multiple functions, or invalid Python.\n\nOUTPUT FORMAT (VERY STRICT):\n- You MUST output EXACTLY one JSON object, NOT wrapped in an array, NOT as a string.\n- First character must be '{' and last character must be '}'.\n- No markdown, no explanations, no extra text.\n\nThe JSON object MUST contain:\n- One field per candidate:\n    \"suggested_score[i][j]\": <number between 0 and 1>\n  where [i][j] matches the indices in \"suggested_code[i][j]\" exactly.\n- Plus two fields:\n    \"best_code_id\": <string or null>\n    \"best_score\": <number between 0 and 1>\n\nCRITICAL RULES FOR best_code_id / best_score:\n- best_code_id MUST be either null OR EXACTLY one of the input candidate IDs,\n  for example: \"suggested_code[0][0]\" or \"suggested_code[2][1]\".\n- NEVER output bare indices such as \"2[0]\", \"[2][0]\", \"0\", or similar.\n- The candidate id is ALWAYS the full string \"suggested_code[i][j]\".\n- best_score MUST be EXACTLY equal to the numeric value of the chosen\n  \"suggested_score[i][j]\" field, not an independent or rounded value.\n\nALLOWED KEYS:\n- Only keys that start with \"suggested_score[\" and end with \"]\" (with indices),\n  plus \"best_code_id\" and \"best_score\".\n- Do NOT output keys such as \"scores\", \"best_candidates\", \"highest_score\",\n  or any other names.\n\nGENERAL RULES:\n- All score values must be numbers between 0 and 1 inclusive.\n- Do not output role/content fields.\n- Do not output markdown code fences, natural-language reasoning, or comments.\n- Your entire response must be exactly one JSON object obeying all rules above."
        },
        {
            "role": "user",
            "content": "{\"chatInput\":\"\\\"\\\"\\\"\\nWrite a function to concatenate each element of tuple by the delimiter.\\nassert concatenate_tuple((\\\"ID\\\", \\\"is\\\", 4, \\\"UTS\\\") ) == 'ID-is-4-UTS'\\n\\\"\\\"\\\"\\n\",\"task_id\":\"Mbpp/421\",\"suggested_code[0][0]\":\"def concatenate_tuple(tup, delimiter):\\n    return delimiter.join(map(str, tup))\",\"suggested_code[0][1]\":\"def concat_with_delimiter(input_tuple, delimiter):\\n    return delimiter.join(input_tuple)\",\"suggested_code[0][2]\":\"def tuple_to_string(tup, sep):\\n    return sep.join(map(str, tup))\",\"suggested_code[1][0]\":\"def concatenate_tuple(input_tuple, delimiter):\\n    return delimiter.join(map(str, input_tuple))\",\"suggested_code[1][1]\":\"def concatenate_tuple(input_tuple, delimiter):\\n    result = ''\\n    for item in input_tuple:\\n        result += str(item) + delimiter\\n    return result[:-len(delimiter)] if input_tuple else ''\",\"suggested_code[1][2]\":\"def concatenate_tuple(input_tuple, delimiter):\\n    return ''.join([str(item) + delimiter for item in input_tuple])[:-len(delimiter)] if input_tuple else ''\",\"suggested_code[2][0]\":\"def concatenate_tuple(input_tuple, delimiter=' '):\\n    return delimiter.join(map(str, input_tuple))\",\"suggested_code[2][1]\":\"def concatenate_tuple(input_tuple, delimiter=','):\\n    return delimiter.join([str(item) for item in input_tuple])\",\"suggested_code[2][2]\":\"def concatenate_tuple(input_tuple, delimiter=''):\\n    return delimiter.join(map(str, input_tuple))\"}"
        }
    ]
}

This took ~20mins

{
    "model": "qwen3:8b",
    "stream": false,
    "keep_alive": "6h",
    "format": {
        "type": "object",
        "properties": {
            "suggested_score[0][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[0][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[0][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "best_code_id": {
                "type": [
                    "string",
                    "null"
                ]
            },
            "best_score": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            }
        },
        "required": [
            "suggested_score[0][0]",
            "suggested_score[0][1]",
            "suggested_score[0][2]",
            "suggested_score[1][0]",
            "suggested_score[1][1]",
            "suggested_score[1][2]",
            "suggested_score[2][0]",
            "suggested_score[2][1]",
            "suggested_score[2][2]",
            "best_code_id",
            "best_score"
        ],
        "additionalProperties": false
    },
    "messages": [
        {
            "role": "system",
            "content": "You are a JSON scoring engine.\n\nINPUT:\n- One JSON object containing:\n  - \"chatInput\" (natural-language task)\n  - Several \"suggested_code[i][j]\" string fields (candidate implementations).\n\nYOUR JOB:\n- For EVERY suggested_code[i][j], produce one numeric score field\n  named \"suggested_score[i][j]\" in [0.0, 1.0].\n- Then choose exactly ONE best candidate and report it in best_code_id and best_score.\n\nSCORING GUIDELINES (brief):\n- 1.0   : very likely fully correct and robust.\n- 0.7–0.9: mostly correct, minor issues.\n- 0.4–0.6: partially correct.\n- 0.1–0.3: clearly flawed but somewhat relevant.\n- 0.0   : invalid, unrelated, or obviously wrong.\n- Score 0.0 if a candidate has markdown fences, long prose, multiple functions, or invalid Python.\n\nOUTPUT FORMAT (VERY STRICT):\n- You MUST output EXACTLY one JSON object, NOT wrapped in an array, NOT as a string.\n- First character must be '{' and last character must be '}'.\n- No markdown, no explanations, no extra text.\n\nThe JSON object MUST contain:\n- One field per candidate:\n    \"suggested_score[i][j]\": <number between 0 and 1>\n  where [i][j] matches the indices in \"suggested_code[i][j]\" exactly.\n- Plus two fields:\n    \"best_code_id\": <string or null>\n    \"best_score\": <number between 0 and 1>\n\nCRITICAL RULES FOR best_code_id / best_score:\n- best_code_id MUST be either null OR EXACTLY one of the input candidate IDs,\n  for example: \"suggested_code[0][0]\" or \"suggested_code[2][1]\".\n- NEVER output bare indices such as \"2[0]\", \"[2][0]\", \"0\", or similar.\n- The candidate id is ALWAYS the full string \"suggested_code[i][j]\".\n- best_score MUST be EXACTLY equal to the numeric value of the chosen\n  \"suggested_score[i][j]\" field, not an independent or rounded value.\n\nALLOWED KEYS:\n- Only keys that start with \"suggested_score[\" and end with \"]\" (with indices),\n  plus \"best_code_id\" and \"best_score\".\n- Do NOT output keys such as \"scores\", \"best_candidates\", \"highest_score\",\n  or any other names.\n\nGENERAL RULES:\n- All score values must be numbers between 0 and 1 inclusive.\n- Do not output role/content fields.\n- Do not output markdown code fences, natural-language reasoning, or comments.\n- Your entire response must be exactly one JSON object obeying all rules above."
        },
        {
            "role": "user",
            "content": "{\"chatInput\":\"\\\"\\\"\\\"\\nWrite a python function to return the negative numbers in a list.\\nassert neg_nos([-1,4,5,-6]) == [-1,-6]\\n\\\"\\\"\\\"\\n\",\"task_id\":\"Mbpp/436\",\"suggested_code[0][0]\":\"def neg_nos(numbers):\\n    return [x for x in numbers if x < 0]\",\"suggested_code[0][1]\":\"def neg_nos(numbers):\\n    result = []\\n    for x in numbers:\\n        if x < 0:\\n            result.append(x)\\n    return result\",\"suggested_code[0][2]\":\"def neg_nos(numbers):\\n    return list(filter(lambda x: x < 0, numbers))\",\"suggested_code[1][0]\":\"def neg_nos(numbers):\\n    return [num for num in numbers if num < 0]\",\"suggested_code[1][1]\":\"def neg_nos(numbers):\\n    result = []\\n    for num in numbers:\\n        if num < 0:\\n            result.append(num)\\n    return result\",\"suggested_code[1][2]\":\"def neg_nos(numbers):\\n    return list(filter(lambda x: x < 0, numbers))\",\"suggested_code[2][0]\":\"def neg_nos(numbers):\\n  return [n for n in numbers if n < 0]\",\"suggested_code[2][1]\":\"def neg_nos(numbers):\\n  negatives = []\\n  for number in numbers:\\n    if number < 0:\\n      negatives.append(number)\\n  return negatives\",\"suggested_code[2][2]\":\"def neg_nos(numbers):\\n  result = []\\n  for n in numbers:\\n    if n < 0:\\n      result.append(n)\\n  return result\"}"
        }
    ]
}

It just timeout for Qwen

{
    "model": "qwen3:8b",
    "stream": false,
    "keep_alive": "6h",
    "format": {
        "type": "object",
        "properties": {
            "suggested_score[0][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[0][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[0][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[1][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][0]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][1]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "suggested_score[2][2]": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            },
            "best_code_id": {
                "type": [
                    "string",
                    "null"
                ]
            },
            "best_score": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
            }
        },
        "required": [
            "suggested_score[0][0]",
            "suggested_score[0][1]",
            "suggested_score[0][2]",
            "suggested_score[1][0]",
            "suggested_score[1][1]",
            "suggested_score[1][2]",
            "suggested_score[2][0]",
            "suggested_score[2][1]",
            "suggested_score[2][2]",
            "best_code_id",
            "best_score"
        ],
        "additionalProperties": false
    },
    "messages": [
        {
            "role": "system",
            "content": "You are a JSON scoring engine.\n\nINPUT:\n- One JSON object containing:\n  - \"chatInput\" (natural-language task)\n  - Several \"suggested_code[i][j]\" string fields (candidate implementations).\n\nYOUR JOB:\n- For EVERY suggested_code[i][j], produce one numeric score field\n  named \"suggested_score[i][j]\" in [0.0, 1.0].\n- Then choose exactly ONE best candidate and report it in best_code_id and best_score.\n\nSCORING GUIDELINES (brief):\n- 1.0   : very likely fully correct and robust.\n- 0.7–0.9: mostly correct, minor issues.\n- 0.4–0.6: partially correct.\n- 0.1–0.3: clearly flawed but somewhat relevant.\n- 0.0   : invalid, unrelated, or obviously wrong.\n- Score 0.0 if a candidate has markdown fences, long prose, multiple functions, or invalid Python.\n\nOUTPUT FORMAT (VERY STRICT):\n- You MUST output EXACTLY one JSON object, NOT wrapped in an array, NOT as a string.\n- First character must be '{' and last character must be '}'.\n- No markdown, no explanations, no extra text.\n\nThe JSON object MUST contain:\n- One field per candidate:\n    \"suggested_score[i][j]\": <number between 0 and 1>\n  where [i][j] matches the indices in \"suggested_code[i][j]\" exactly.\n- Plus two fields:\n    \"best_code_id\": <string or null>\n    \"best_score\": <number between 0 and 1>\n\nCRITICAL RULES FOR best_code_id / best_score:\n- best_code_id MUST be either null OR EXACTLY one of the input candidate IDs,\n  for example: \"suggested_code[0][0]\" or \"suggested_code[2][1]\".\n- NEVER output bare indices such as \"2[0]\", \"[2][0]\", \"0\", or similar.\n- The candidate id is ALWAYS the full string \"suggested_code[i][j]\".\n- best_score MUST be EXACTLY equal to the numeric value of the chosen\n  \"suggested_score[i][j]\" field, not an independent or rounded value.\n\nALLOWED KEYS:\n- Only keys that start with \"suggested_score[\" and end with \"]\" (with indices),\n  plus \"best_code_id\" and \"best_score\".\n- Do NOT output keys such as \"scores\", \"best_candidates\", \"highest_score\",\n  or any other names.\n\nGENERAL RULES:\n- All score values must be numbers between 0 and 1 inclusive.\n- Do not output role/content fields.\n- Do not output markdown code fences, natural-language reasoning, or comments.\n- Your entire response must be exactly one JSON object obeying all rules above."
        },
        {
            "role": "user",
            "content": "{\"chatInput\":\"\\\"\\\"\\\"\\nWrite a function to find the first adverb and their positions in a given sentence.\\nassert find_adverb_position(\\\"clearly!! we can see the sky\\\")==(0, 7, 'clearly')\\n\\\"\\\"\\\"\\n\",\"task_id\":\"Mbpp/440\",\"suggested_code[0][0]\":\"def find_adverb_position(sentence):\\n    adverbs = ['quickly', 'slowly', 'carefully', 'gently', 'lightly', 'heavily', 'firmly', 'strictly']\\n    for i in range(len(sentence)):\\n        if sentence[i:i+4].lower() in adverbs:\\n            return (sentence.rfind(' ', 0, i), sentence.find(' ', i), sentence[i:i+4])\\n    return None\",\"suggested_code[0][1]\":\"def find_adverb_position(sentence):\\n    import re\\n    match = re.search(r'(quickly|slowly|carefully|gently|lightly|heavily|firmly|strictly)', sentence, re.IGNORECASE)\\n    return (match.start() if match else None, match.end() if match else None, match.group(0)) if match else (None, None, None)\",\"suggested_code[0][2]\":\"def find_adverb_position(sentence):\\n    adverbs = ['quickly', 'slowly']\\n    for i in range(len(sentence)):\\n        word = sentence[i:i+4].lower()\\n        if word in adverbs or word.endswith('ly') and sentence[i-1] + word[:-1] in adverbs:\\n            return (sentence.rfind(' ', 0, i), sentence.find(' ', i), word)\",\"suggested_code[1][0]\":\"def find_adverb_position(sentence):\\n    words = sentence.split()\\n    start = 0\\n    for word in words:\\n        stripped = word.rstrip('!@#$%^&*()-_+=[]{}|;:,.<>?')\\n        if stripped and stripped.endswith('ly'):\\n            return (start, start + len(stripped), stripped)\\n        start += len(word) + 1\\n    return None\",\"suggested_code[1][1]\":\"def find_adverb_position(sentence):\\n    words = sentence.split()\\n    start = 0\\n    for word in words:\\n        stripped = word.rstrip('!@#$%^&*()-_+=[]{}|;:,.<>?')\\n        if stripped and stripped.endswith('ly'):\\n            return (start, start + len(stripped), stripped)\\n        start += len(word) + 1\\n    return None\",\"suggested_code[1][2]\":\"def find_adverb_position(sentence):\\n    words = sentence.split()\\n    start = 0\\n    for word in words:\\n        stripped = word.rstrip('!@#$%^&*()-_+=[]{}|;:,.<>?')\\n        if stripped and stripped.endswith('ly'):\\n            return (start, start + len(stripped), stripped)\\n        start += len(word) + 1\\n    return None\",\"suggested_code[2][0]\":\"\\ndef find_adverb_position(sentence):\\n    words = sentence.split()\\n    adverbs = [word.strip('.,!?') for word in words if word.endswith(('ly', 'y'))]\\n    if not adverbs:\\n        return None\\n    adverb = adverbs[0]\\n    start_pos = sentence.find(adverb)\\n    end_pos = start_pos + len(adverb)\\n    return (start_pos, end_pos, adverb)\",\"suggested_code[2][1]\":\"\\ndef find_adverb_position(sentence):\\n    for i, word in enumerate(sentence.split()):\\n        clean_word = word.strip('.,!?')\\n        if clean_word.endswith(('ly', 'y')):\\n            start_pos = sentence.find(clean_word)\\n            end_pos = start_pos + len(clean_word)\\n            return (start_pos, end_pos, clean_word)\",\"suggested_code[2][2]\":\"\\ndef find_adverb_position(sentence):\\n    import re\\n    match = re.search(r'\\\\b(\\\\w+ly|\\\\w+y)\\\\b', sentence)\\n    if not match:\\n        return None\\n    adverb = match.group()\\n    start_pos, end_pos = match.span()\\n    return (start_pos, end_pos, adverb)\"}"
        }
    ]
}
<!-- gh-comment-id:3705984138 --> @Bcwz commented on GitHub (Jan 2, 2026): > Can you share a prompt? This took ~8mins. ``` { "model": "phi4:14b", "stream": false, "keep_alive": "6h", "format": { "type": "object", "properties": { "requirements_brief": { "type": "object", "properties": { "summary": { "type": "string" }, "assumptions": { "type": "array", "items": { "type": "string" } }, "non_goals": { "type": "array", "items": { "type": "string" } }, "constraints": { "type": "object" } }, "required": [ "summary", "assumptions", "non_goals", "constraints" ], "additionalProperties": false }, "subtasks": { "type": "array", "items": { "type": "object", "properties": { "id": { "type": "string" }, "purpose": { "type": "string" }, "language": { "type": "string" }, "inputs": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "type": { "type": "string" }, "description": { "type": "string" } }, "required": [ "name", "type", "description" ], "additionalProperties": false } }, "outputs": { "type": "array", "items": { "type": "object", "properties": { "type": { "type": "string" }, "description": { "type": "string" } }, "required": [ "type", "description" ], "additionalProperties": false } }, "acceptance_criteria": { "type": "array", "items": { "type": "string" } } }, "required": [ "id", "purpose", "language", "inputs", "outputs", "acceptance_criteria" ], "additionalProperties": false } } }, "required": [ "requirements_brief", "subtasks" ], "additionalProperties": false }, "messages": [ { "role": "system", "content": "You are Task Breakdown Agent (AGENT_ID=TASKBREAKDOWN_AGENT_1).\n\nMISSION:\n- Read chatInput from the FIRST element of the input array.\n- Normalize it into a requirements_brief and a list of subtasks.\n\nOUTPUT CONTRACT (STRICT):\n- Respond with exactly one JSON object.\n- Top-level keys must be only \"requirements_brief\" and \"subtasks\".\n- No markdown, no prose, no code fences, no tags, no text outside the JSON.\n- First output character must be '{' and the last must be '}'.\n\nrequirements_brief:\n- Must contain exactly: summary (string), assumptions (string array), non_goals (string array), constraints (object).\n- No extra keys.\n\nsubtasks:\n- An array with at least one element.\n- Each subtask must contain exactly: id, purpose, language, inputs, outputs, acceptance_criteria.\n- id: stable, unique identifiers like \"ST-001\", \"ST-002\", ...\n- language: programming language, default to \"python\" if not specified in chatInput.\n- inputs: array of objects {name, type, description}.\n- outputs: array of objects {type, description}.\n- acceptance_criteria: array of clear, testable statements (Given/When/Then style allowed).\n- No additional keys such as title, agent, dispatch, text, or output.\n\nINPUT FORMAT:\n- You receive a JSON array, for example: [{\"chatInput\": \"...\"}].\n- Use ONLY chatInput from the first element and ignore all other fields.\n\nSTYLE RULES:\n- Do not wrap the output JSON in an extra array or object.\n- Do not include chain-of-thought, explanations, comments, or reasoning.\n- Do not output tokens like <think>, </think>, <analysis>, or similar.\n- Ensure the JSON is syntactically valid with no trailing commas.\n" }, { "role": "user", "content": "\"\"\"\nWrite a python function to check whether any value in a sequence exists in a sequence or not.\nassert overlapping([1,2,3,4,5],[6,7,8,9]) == False\n\"\"\"\n" } ] } ``` This took ~20mins ``` { "model": "phi4:14b", "stream": false, "keep_alive": "6h", "format": { "type": "object", "properties": { "suggested_score[0][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[0][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[0][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "best_code_id": { "type": [ "string", "null" ] }, "best_score": { "type": "number", "minimum": 0, "maximum": 1 } }, "required": [ "suggested_score[0][0]", "suggested_score[0][1]", "suggested_score[0][2]", "suggested_score[1][0]", "suggested_score[1][1]", "suggested_score[1][2]", "suggested_score[2][0]", "suggested_score[2][1]", "suggested_score[2][2]", "best_code_id", "best_score" ], "additionalProperties": false }, "messages": [ { "role": "system", "content": "You are a JSON scoring engine.\n\nINPUT:\n- One JSON object containing:\n - \"chatInput\" (natural-language task)\n - Several \"suggested_code[i][j]\" string fields (candidate implementations).\n\nYOUR JOB:\n- For EVERY suggested_code[i][j], produce one numeric score field\n named \"suggested_score[i][j]\" in [0.0, 1.0].\n- Then choose exactly ONE best candidate and report it in best_code_id and best_score.\n\nSCORING GUIDELINES (brief):\n- 1.0 : very likely fully correct and robust.\n- 0.7–0.9: mostly correct, minor issues.\n- 0.4–0.6: partially correct.\n- 0.1–0.3: clearly flawed but somewhat relevant.\n- 0.0 : invalid, unrelated, or obviously wrong.\n- Score 0.0 if a candidate has markdown fences, long prose, multiple functions, or invalid Python.\n\nOUTPUT FORMAT (VERY STRICT):\n- You MUST output EXACTLY one JSON object, NOT wrapped in an array, NOT as a string.\n- First character must be '{' and last character must be '}'.\n- No markdown, no explanations, no extra text.\n\nThe JSON object MUST contain:\n- One field per candidate:\n \"suggested_score[i][j]\": <number between 0 and 1>\n where [i][j] matches the indices in \"suggested_code[i][j]\" exactly.\n- Plus two fields:\n \"best_code_id\": <string or null>\n \"best_score\": <number between 0 and 1>\n\nCRITICAL RULES FOR best_code_id / best_score:\n- best_code_id MUST be either null OR EXACTLY one of the input candidate IDs,\n for example: \"suggested_code[0][0]\" or \"suggested_code[2][1]\".\n- NEVER output bare indices such as \"2[0]\", \"[2][0]\", \"0\", or similar.\n- The candidate id is ALWAYS the full string \"suggested_code[i][j]\".\n- best_score MUST be EXACTLY equal to the numeric value of the chosen\n \"suggested_score[i][j]\" field, not an independent or rounded value.\n\nALLOWED KEYS:\n- Only keys that start with \"suggested_score[\" and end with \"]\" (with indices),\n plus \"best_code_id\" and \"best_score\".\n- Do NOT output keys such as \"scores\", \"best_candidates\", \"highest_score\",\n or any other names.\n\nGENERAL RULES:\n- All score values must be numbers between 0 and 1 inclusive.\n- Do not output role/content fields.\n- Do not output markdown code fences, natural-language reasoning, or comments.\n- Your entire response must be exactly one JSON object obeying all rules above." }, { "role": "user", "content": "{\"chatInput\":\"\\\"\\\"\\\"\\nWrite a function to concatenate each element of tuple by the delimiter.\\nassert concatenate_tuple((\\\"ID\\\", \\\"is\\\", 4, \\\"UTS\\\") ) == 'ID-is-4-UTS'\\n\\\"\\\"\\\"\\n\",\"task_id\":\"Mbpp/421\",\"suggested_code[0][0]\":\"def concatenate_tuple(tup, delimiter):\\n return delimiter.join(map(str, tup))\",\"suggested_code[0][1]\":\"def concat_with_delimiter(input_tuple, delimiter):\\n return delimiter.join(input_tuple)\",\"suggested_code[0][2]\":\"def tuple_to_string(tup, sep):\\n return sep.join(map(str, tup))\",\"suggested_code[1][0]\":\"def concatenate_tuple(input_tuple, delimiter):\\n return delimiter.join(map(str, input_tuple))\",\"suggested_code[1][1]\":\"def concatenate_tuple(input_tuple, delimiter):\\n result = ''\\n for item in input_tuple:\\n result += str(item) + delimiter\\n return result[:-len(delimiter)] if input_tuple else ''\",\"suggested_code[1][2]\":\"def concatenate_tuple(input_tuple, delimiter):\\n return ''.join([str(item) + delimiter for item in input_tuple])[:-len(delimiter)] if input_tuple else ''\",\"suggested_code[2][0]\":\"def concatenate_tuple(input_tuple, delimiter=' '):\\n return delimiter.join(map(str, input_tuple))\",\"suggested_code[2][1]\":\"def concatenate_tuple(input_tuple, delimiter=','):\\n return delimiter.join([str(item) for item in input_tuple])\",\"suggested_code[2][2]\":\"def concatenate_tuple(input_tuple, delimiter=''):\\n return delimiter.join(map(str, input_tuple))\"}" } ] } ``` This took ~20mins ``` { "model": "qwen3:8b", "stream": false, "keep_alive": "6h", "format": { "type": "object", "properties": { "suggested_score[0][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[0][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[0][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "best_code_id": { "type": [ "string", "null" ] }, "best_score": { "type": "number", "minimum": 0, "maximum": 1 } }, "required": [ "suggested_score[0][0]", "suggested_score[0][1]", "suggested_score[0][2]", "suggested_score[1][0]", "suggested_score[1][1]", "suggested_score[1][2]", "suggested_score[2][0]", "suggested_score[2][1]", "suggested_score[2][2]", "best_code_id", "best_score" ], "additionalProperties": false }, "messages": [ { "role": "system", "content": "You are a JSON scoring engine.\n\nINPUT:\n- One JSON object containing:\n - \"chatInput\" (natural-language task)\n - Several \"suggested_code[i][j]\" string fields (candidate implementations).\n\nYOUR JOB:\n- For EVERY suggested_code[i][j], produce one numeric score field\n named \"suggested_score[i][j]\" in [0.0, 1.0].\n- Then choose exactly ONE best candidate and report it in best_code_id and best_score.\n\nSCORING GUIDELINES (brief):\n- 1.0 : very likely fully correct and robust.\n- 0.7–0.9: mostly correct, minor issues.\n- 0.4–0.6: partially correct.\n- 0.1–0.3: clearly flawed but somewhat relevant.\n- 0.0 : invalid, unrelated, or obviously wrong.\n- Score 0.0 if a candidate has markdown fences, long prose, multiple functions, or invalid Python.\n\nOUTPUT FORMAT (VERY STRICT):\n- You MUST output EXACTLY one JSON object, NOT wrapped in an array, NOT as a string.\n- First character must be '{' and last character must be '}'.\n- No markdown, no explanations, no extra text.\n\nThe JSON object MUST contain:\n- One field per candidate:\n \"suggested_score[i][j]\": <number between 0 and 1>\n where [i][j] matches the indices in \"suggested_code[i][j]\" exactly.\n- Plus two fields:\n \"best_code_id\": <string or null>\n \"best_score\": <number between 0 and 1>\n\nCRITICAL RULES FOR best_code_id / best_score:\n- best_code_id MUST be either null OR EXACTLY one of the input candidate IDs,\n for example: \"suggested_code[0][0]\" or \"suggested_code[2][1]\".\n- NEVER output bare indices such as \"2[0]\", \"[2][0]\", \"0\", or similar.\n- The candidate id is ALWAYS the full string \"suggested_code[i][j]\".\n- best_score MUST be EXACTLY equal to the numeric value of the chosen\n \"suggested_score[i][j]\" field, not an independent or rounded value.\n\nALLOWED KEYS:\n- Only keys that start with \"suggested_score[\" and end with \"]\" (with indices),\n plus \"best_code_id\" and \"best_score\".\n- Do NOT output keys such as \"scores\", \"best_candidates\", \"highest_score\",\n or any other names.\n\nGENERAL RULES:\n- All score values must be numbers between 0 and 1 inclusive.\n- Do not output role/content fields.\n- Do not output markdown code fences, natural-language reasoning, or comments.\n- Your entire response must be exactly one JSON object obeying all rules above." }, { "role": "user", "content": "{\"chatInput\":\"\\\"\\\"\\\"\\nWrite a python function to return the negative numbers in a list.\\nassert neg_nos([-1,4,5,-6]) == [-1,-6]\\n\\\"\\\"\\\"\\n\",\"task_id\":\"Mbpp/436\",\"suggested_code[0][0]\":\"def neg_nos(numbers):\\n return [x for x in numbers if x < 0]\",\"suggested_code[0][1]\":\"def neg_nos(numbers):\\n result = []\\n for x in numbers:\\n if x < 0:\\n result.append(x)\\n return result\",\"suggested_code[0][2]\":\"def neg_nos(numbers):\\n return list(filter(lambda x: x < 0, numbers))\",\"suggested_code[1][0]\":\"def neg_nos(numbers):\\n return [num for num in numbers if num < 0]\",\"suggested_code[1][1]\":\"def neg_nos(numbers):\\n result = []\\n for num in numbers:\\n if num < 0:\\n result.append(num)\\n return result\",\"suggested_code[1][2]\":\"def neg_nos(numbers):\\n return list(filter(lambda x: x < 0, numbers))\",\"suggested_code[2][0]\":\"def neg_nos(numbers):\\n return [n for n in numbers if n < 0]\",\"suggested_code[2][1]\":\"def neg_nos(numbers):\\n negatives = []\\n for number in numbers:\\n if number < 0:\\n negatives.append(number)\\n return negatives\",\"suggested_code[2][2]\":\"def neg_nos(numbers):\\n result = []\\n for n in numbers:\\n if n < 0:\\n result.append(n)\\n return result\"}" } ] } ``` It just timeout for Qwen ``` { "model": "qwen3:8b", "stream": false, "keep_alive": "6h", "format": { "type": "object", "properties": { "suggested_score[0][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[0][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[0][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[1][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][0]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][1]": { "type": "number", "minimum": 0, "maximum": 1 }, "suggested_score[2][2]": { "type": "number", "minimum": 0, "maximum": 1 }, "best_code_id": { "type": [ "string", "null" ] }, "best_score": { "type": "number", "minimum": 0, "maximum": 1 } }, "required": [ "suggested_score[0][0]", "suggested_score[0][1]", "suggested_score[0][2]", "suggested_score[1][0]", "suggested_score[1][1]", "suggested_score[1][2]", "suggested_score[2][0]", "suggested_score[2][1]", "suggested_score[2][2]", "best_code_id", "best_score" ], "additionalProperties": false }, "messages": [ { "role": "system", "content": "You are a JSON scoring engine.\n\nINPUT:\n- One JSON object containing:\n - \"chatInput\" (natural-language task)\n - Several \"suggested_code[i][j]\" string fields (candidate implementations).\n\nYOUR JOB:\n- For EVERY suggested_code[i][j], produce one numeric score field\n named \"suggested_score[i][j]\" in [0.0, 1.0].\n- Then choose exactly ONE best candidate and report it in best_code_id and best_score.\n\nSCORING GUIDELINES (brief):\n- 1.0 : very likely fully correct and robust.\n- 0.7–0.9: mostly correct, minor issues.\n- 0.4–0.6: partially correct.\n- 0.1–0.3: clearly flawed but somewhat relevant.\n- 0.0 : invalid, unrelated, or obviously wrong.\n- Score 0.0 if a candidate has markdown fences, long prose, multiple functions, or invalid Python.\n\nOUTPUT FORMAT (VERY STRICT):\n- You MUST output EXACTLY one JSON object, NOT wrapped in an array, NOT as a string.\n- First character must be '{' and last character must be '}'.\n- No markdown, no explanations, no extra text.\n\nThe JSON object MUST contain:\n- One field per candidate:\n \"suggested_score[i][j]\": <number between 0 and 1>\n where [i][j] matches the indices in \"suggested_code[i][j]\" exactly.\n- Plus two fields:\n \"best_code_id\": <string or null>\n \"best_score\": <number between 0 and 1>\n\nCRITICAL RULES FOR best_code_id / best_score:\n- best_code_id MUST be either null OR EXACTLY one of the input candidate IDs,\n for example: \"suggested_code[0][0]\" or \"suggested_code[2][1]\".\n- NEVER output bare indices such as \"2[0]\", \"[2][0]\", \"0\", or similar.\n- The candidate id is ALWAYS the full string \"suggested_code[i][j]\".\n- best_score MUST be EXACTLY equal to the numeric value of the chosen\n \"suggested_score[i][j]\" field, not an independent or rounded value.\n\nALLOWED KEYS:\n- Only keys that start with \"suggested_score[\" and end with \"]\" (with indices),\n plus \"best_code_id\" and \"best_score\".\n- Do NOT output keys such as \"scores\", \"best_candidates\", \"highest_score\",\n or any other names.\n\nGENERAL RULES:\n- All score values must be numbers between 0 and 1 inclusive.\n- Do not output role/content fields.\n- Do not output markdown code fences, natural-language reasoning, or comments.\n- Your entire response must be exactly one JSON object obeying all rules above." }, { "role": "user", "content": "{\"chatInput\":\"\\\"\\\"\\\"\\nWrite a function to find the first adverb and their positions in a given sentence.\\nassert find_adverb_position(\\\"clearly!! we can see the sky\\\")==(0, 7, 'clearly')\\n\\\"\\\"\\\"\\n\",\"task_id\":\"Mbpp/440\",\"suggested_code[0][0]\":\"def find_adverb_position(sentence):\\n adverbs = ['quickly', 'slowly', 'carefully', 'gently', 'lightly', 'heavily', 'firmly', 'strictly']\\n for i in range(len(sentence)):\\n if sentence[i:i+4].lower() in adverbs:\\n return (sentence.rfind(' ', 0, i), sentence.find(' ', i), sentence[i:i+4])\\n return None\",\"suggested_code[0][1]\":\"def find_adverb_position(sentence):\\n import re\\n match = re.search(r'(quickly|slowly|carefully|gently|lightly|heavily|firmly|strictly)', sentence, re.IGNORECASE)\\n return (match.start() if match else None, match.end() if match else None, match.group(0)) if match else (None, None, None)\",\"suggested_code[0][2]\":\"def find_adverb_position(sentence):\\n adverbs = ['quickly', 'slowly']\\n for i in range(len(sentence)):\\n word = sentence[i:i+4].lower()\\n if word in adverbs or word.endswith('ly') and sentence[i-1] + word[:-1] in adverbs:\\n return (sentence.rfind(' ', 0, i), sentence.find(' ', i), word)\",\"suggested_code[1][0]\":\"def find_adverb_position(sentence):\\n words = sentence.split()\\n start = 0\\n for word in words:\\n stripped = word.rstrip('!@#$%^&*()-_+=[]{}|;:,.<>?')\\n if stripped and stripped.endswith('ly'):\\n return (start, start + len(stripped), stripped)\\n start += len(word) + 1\\n return None\",\"suggested_code[1][1]\":\"def find_adverb_position(sentence):\\n words = sentence.split()\\n start = 0\\n for word in words:\\n stripped = word.rstrip('!@#$%^&*()-_+=[]{}|;:,.<>?')\\n if stripped and stripped.endswith('ly'):\\n return (start, start + len(stripped), stripped)\\n start += len(word) + 1\\n return None\",\"suggested_code[1][2]\":\"def find_adverb_position(sentence):\\n words = sentence.split()\\n start = 0\\n for word in words:\\n stripped = word.rstrip('!@#$%^&*()-_+=[]{}|;:,.<>?')\\n if stripped and stripped.endswith('ly'):\\n return (start, start + len(stripped), stripped)\\n start += len(word) + 1\\n return None\",\"suggested_code[2][0]\":\"\\ndef find_adverb_position(sentence):\\n words = sentence.split()\\n adverbs = [word.strip('.,!?') for word in words if word.endswith(('ly', 'y'))]\\n if not adverbs:\\n return None\\n adverb = adverbs[0]\\n start_pos = sentence.find(adverb)\\n end_pos = start_pos + len(adverb)\\n return (start_pos, end_pos, adverb)\",\"suggested_code[2][1]\":\"\\ndef find_adverb_position(sentence):\\n for i, word in enumerate(sentence.split()):\\n clean_word = word.strip('.,!?')\\n if clean_word.endswith(('ly', 'y')):\\n start_pos = sentence.find(clean_word)\\n end_pos = start_pos + len(clean_word)\\n return (start_pos, end_pos, clean_word)\",\"suggested_code[2][2]\":\"\\ndef find_adverb_position(sentence):\\n import re\\n match = re.search(r'\\\\b(\\\\w+ly|\\\\w+y)\\\\b', sentence)\\n if not match:\\n return None\\n adverb = match.group()\\n start_pos, end_pos = match.span()\\n return (start_pos, end_pos, adverb)\"}" } ] } ```
GiteaMirror commented 2026-04-22 18:26:34 -05:00
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@rick-github commented on GitHub (Jan 4, 2026):

Thanks for supplying full requests.

I ran these on some machines and got the following tokens/sec:

prompt 3080 4070 5060 AMD 8060 6000 pro
1 41.89 48.41 42.94 20.05 136.83
2 41.5 48.16 42.91 19.49 135.8
3 67.79 78.31 69.71 24.7 180.85
4 62.33 69.68 65.37 20.24 167.28

Contrast this with the results from your original post where the qwen3:8b model returned 8.38 t/s and the phi4:14b model returned 2.91 t/s. I believe that the DGX Spark has a unified memory like the 8060 with a Grace Blackwell GPU like the 6000 pro, so I would expect it to perform better than the 8060.

If the GPU is running slow, this would account for the timeouts on the qwen3:8b model: it generates thinking tokens, so takes a lot longer to finish the response to a query. You can check this by setting "stream":true in the request and check the time taken between tokens.

Since you mentioned that it gets worse over time, I wonder if the GPU is power/temperature throttling itself, resulting in lower performance. When you are running your prompts, what's the output of

nvidia-smi -q -d POWER,TEMPERATURE,PERFORMANCE
<!-- gh-comment-id:3707612054 --> @rick-github commented on GitHub (Jan 4, 2026): Thanks for supplying full requests. I ran these on some machines and got the following tokens/sec: prompt | 3080 | 4070 | 5060 | AMD 8060 | 6000 pro | | -- | -- |-- | -- | -- | -- | | 1 | 41.89 | 48.41 | 42.94 | 20.05 | 136.83 | | 2 | 41.5 | 48.16 |42.91 | 19.49 | 135.8 | | 3 | 67.79 | 78.31 |69.71 | 24.7 | 180.85 | | 4 |62.33 | 69.68 |65.37 | 20.24 | 167.28 | Contrast this with the results from your original post where the qwen3:8b model returned 8.38 t/s and the phi4:14b model returned 2.91 t/s. I believe that the DGX Spark has a unified memory like the 8060 with a Grace Blackwell GPU like the 6000 pro, so I would expect it to perform better than the 8060. If the GPU is running slow, this would account for the timeouts on the qwen3:8b model: it generates thinking tokens, so takes a lot longer to finish the response to a query. You can check this by setting `"stream":true` in the request and check the time taken between tokens. Since you mentioned that it gets worse over time, I wonder if the GPU is power/temperature throttling itself, resulting in lower performance. When you are running your prompts, what's the output of ``` nvidia-smi -q -d POWER,TEMPERATURE,PERFORMANCE ```
GiteaMirror commented 2026-04-22 18:26:35 -05:00
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@Bcwz commented on GitHub (Jan 4, 2026):

Contrast this with the results from your original post where the qwen3:8b model returned 8.38 t/s and the phi4:14b model returned 2.91 t/s. I believe that the DGX Spark has a unified memory like the 8060 with a Grace Blackwell GPU like the 6000 pro, so I would expect it to perform better than the 8060.

If the GPU is running slow, this would account for the timeouts on the qwen3:8b model: it generates thinking tokens, so takes a lot longer to finish the response to a query. You can check this by setting "stream":true in the request and check the time taken between tokens.

Yes, i would expect better results from DGX spark.
I don't see why DGX Spark is 'running slow'

Setting 'stream:true' would return unusable data, something like this.
My workflow requires the output format when using 'stream:false'

[
    {
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the\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.362222419Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" chat\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.437238116Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"Input\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.518471498Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.596253575Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" a\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.700801933Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" code\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.79261099Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" request\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.892015636Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\".\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.975847114Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Provide\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.074759215Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" requirements\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.163417537Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_br\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.234526149Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"ief\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.31488186Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.382729849Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" summary\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.471563591Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.538633615Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \\\"\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.629378621Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"Implement\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.717001401Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" a\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.815181825Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" 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is\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.913843104Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" positive\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.001015694Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" integer\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.086707651Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"?\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.169090137Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" The\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.24624024Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" 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integer\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.166163226Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \\u003e=\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.240491833Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.30675467Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"0\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.396461635Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\".\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.459204607Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Non\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.538940649Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_go\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.622782366Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"als\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.718150729Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.795054808Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" not\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.891630949Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" 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requirement\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.784208421Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" to\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.880114198Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" handle\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.962952743Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" negative\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.062967221Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" or\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.144456522Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" non\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.225385244Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"-in\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.29073958Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"teger\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.377839128Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\".\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.446029459Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Constraints\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.522998246Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.591279838Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" maybe\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.690346005Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" time\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:04.770222722Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" 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]\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:42.645329519Z\",\"message\":{\"role\":\"assistant\",\"content\":\"}\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:42.690074658Z\",\"message\":{\"role\":\"assistant\",\"content\":\"]}\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:42.749440162Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\"},\"done\":true,\"done_reason\":\"stop\",\"total_duration\":65326866395,\"load_duration\":223901801,\"prompt_eval_count\":792,\"prompt_eval_duration\":641355471,\"eval_count\":265,\"eval_duration\":14463715540}\n"
    }
]

Since you mentioned that it gets worse over time, I wonder if the GPU is power/temperature throttling itself, resulting in lower performance. When you are running your prompts, what's the output of

nvidia-smi -q -d POWER,TEMPERATURE,PERFORMANCE

I'm running the models overnight and these are the results

==============NVSMI LOG==============

Timestamp                                 : Sun Jan  4 04:51:39 2026
Driver Version                            : 580.95.05
CUDA Version                              : 13.0

Attached GPUs                             : 1
GPU 0000000F:01:00.0
    Performance State                     : P0
    Clocks Event Reasons
        Idle                              : Not Active
        Applications Clocks Setting       : Not Active
        SW Power Cap                      : Not Active
        HW Slowdown                       : Not Active
            HW Thermal Slowdown           : Not Active
            HW Power Brake Slowdown       : Not Active
        Sync Boost                        : Not Active
        SW Thermal Slowdown               : Not Active
        Display Clock Setting             : Not Active
    Clocks Event Reasons Counters
        SW Power Capping                  : 165687 us
        Sync Boost                        : 0 us
        SW Thermal Slowdown               : 903158 us
        HW Thermal Slowdown               : 0 us
        HW Power Braking                  : 0 us
    Sparse Operation Mode                 : N/A
    Temperature
        GPU Current Temp                  : 81 C
        GPU T.Limit Temp                  : 13 C
        GPU Shutdown T.Limit Temp         : -5 C
        GPU Slowdown T.Limit Temp         : -2 C
        GPU Max Operating T.Limit Temp    : 0 C
        GPU Target Temperature            : N/A
        Memory Current Temp               : N/A
        Memory Max Operating T.Limit Temp : N/A
    GPU Power Readings
        Average Power Draw                : 48.31 W
        Instantaneous Power Draw          : 43.04 W
        Current Power Limit               : N/A
        Requested Power Limit             : N/A
        Default Power Limit               : N/A
        Min Power Limit                   : N/A
        Max Power Limit                   : N/A
    Power Samples
        Duration                          : Not Found
        Number of Samples                 : Not Found
        Max                               : Not Found
        Min                               : Not Found
        Avg                               : Not Found
    GPU Memory Power Readings 
        Average Power Draw                : N/A
        Instantaneous Power Draw          : N/A
    Module Power Readings
        Average Power Draw                : N/A
        Instantaneous Power Draw          : N/A
        Current Power Limit               : N/A
        Requested Power Limit             : N/A
        Default Power Limit               : N/A
        Min Power Limit                   : N/A
        Max Power Limit                   : N/A
<!-- gh-comment-id:3707669009 --> @Bcwz commented on GitHub (Jan 4, 2026): > Contrast this with the results from your original post where the qwen3:8b model returned 8.38 t/s and the phi4:14b model returned 2.91 t/s. I believe that the DGX Spark has a unified memory like the 8060 with a Grace Blackwell GPU like the 6000 pro, so I would expect it to perform better than the 8060. > > If the GPU is running slow, this would account for the timeouts on the qwen3:8b model: it generates thinking tokens, so takes a lot longer to finish the response to a query. You can check this by setting `"stream":true` in the request and check the time taken between tokens. > Yes, i would expect better results from DGX spark. I don't see why DGX Spark is 'running slow' Setting 'stream:true' would return unusable data, something like this. My workflow requires the output format when using 'stream:false' ``` [ { "data": "{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:56.935628348Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"We\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.044012673Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" need\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.126185281Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" to\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.204675483Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" parse\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.274594554Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" the\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.362222419Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" chat\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.437238116Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"Input\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.518471498Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.596253575Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" a\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.700801933Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" code\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.79261099Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" request\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.892015636Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\".\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:57.975847114Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Provide\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.074759215Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" requirements\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.163417537Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_br\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.234526149Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"ief\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.31488186Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.382729849Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" summary\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.471563591Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.538633615Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \\\"\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.629378621Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"Implement\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.717001401Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" a\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.815181825Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Python\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.899313603Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" function\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:58.992133935Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" highest\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.086684963Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_P\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.169641205Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"ower\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.23489342Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_of\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.314017981Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.390823382Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"2\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.467350727Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" that\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.534053034Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" returns\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.633563556Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" the\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.719127142Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" greatest\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.805969561Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" power\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.901666513Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" of\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:57:59.994417217Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.08583535Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"2\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.16152963Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \\u003c=\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.240768463Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" n\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.323599543Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\".\\\"\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.389782201Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Ass\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.469971875Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"um\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.545468176Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"ptions\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.644099475Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.725374417Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" input\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.815104176Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" is\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:00.913843104Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" positive\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.001015694Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" integer\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.086707651Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"?\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.169090137Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" The\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.24624024Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" 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we\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.721525913Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" can\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.815610416Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" assume\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.906571251Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" n\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:01.986347561Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" is\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.088785206Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" integer\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.166163226Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \\u003e=\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.240491833Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" \"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.30675467Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"0\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.396461635Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\".\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.459204607Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Non\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.538940649Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"_go\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.622782366Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"als\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.718150729Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\":\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.795054808Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" not\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.891630949Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" required\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:02.98199481Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" to\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.074610923Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" handle\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.142961583Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" floats\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.228060201Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" or\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.296374046Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" negative\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.370066831Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\"?\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.438457234Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" Might\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.523970002Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" say\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.598006874Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" no\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:03.688878502Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\",\"thinking\":\" 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]\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:42.645329519Z\",\"message\":{\"role\":\"assistant\",\"content\":\"}\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:42.690074658Z\",\"message\":{\"role\":\"assistant\",\"content\":\"]}\"},\"done\":false}\n{\"model\":\"gpt-oss:20b\",\"created_at\":\"2026-01-04T04:58:42.749440162Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\"},\"done\":true,\"done_reason\":\"stop\",\"total_duration\":65326866395,\"load_duration\":223901801,\"prompt_eval_count\":792,\"prompt_eval_duration\":641355471,\"eval_count\":265,\"eval_duration\":14463715540}\n" } ] ``` > Since you mentioned that it gets worse over time, I wonder if the GPU is power/temperature throttling itself, resulting in lower performance. When you are running your prompts, what's the output of > > ``` > nvidia-smi -q -d POWER,TEMPERATURE,PERFORMANCE > ``` I'm running the models overnight and these are the results ``` ==============NVSMI LOG============== Timestamp : Sun Jan 4 04:51:39 2026 Driver Version : 580.95.05 CUDA Version : 13.0 Attached GPUs : 1 GPU 0000000F:01:00.0 Performance State : P0 Clocks Event Reasons Idle : Not Active Applications Clocks Setting : Not Active SW Power Cap : Not Active HW Slowdown : Not Active HW Thermal Slowdown : Not Active HW Power Brake Slowdown : Not Active Sync Boost : Not Active SW Thermal Slowdown : Not Active Display Clock Setting : Not Active Clocks Event Reasons Counters SW Power Capping : 165687 us Sync Boost : 0 us SW Thermal Slowdown : 903158 us HW Thermal Slowdown : 0 us HW Power Braking : 0 us Sparse Operation Mode : N/A Temperature GPU Current Temp : 81 C GPU T.Limit Temp : 13 C GPU Shutdown T.Limit Temp : -5 C GPU Slowdown T.Limit Temp : -2 C GPU Max Operating T.Limit Temp : 0 C GPU Target Temperature : N/A Memory Current Temp : N/A Memory Max Operating T.Limit Temp : N/A GPU Power Readings Average Power Draw : 48.31 W Instantaneous Power Draw : 43.04 W Current Power Limit : N/A Requested Power Limit : N/A Default Power Limit : N/A Min Power Limit : N/A Max Power Limit : N/A Power Samples Duration : Not Found Number of Samples : Not Found Max : Not Found Min : Not Found Avg : Not Found GPU Memory Power Readings Average Power Draw : N/A Instantaneous Power Draw : N/A Module Power Readings Average Power Draw : N/A Instantaneous Power Draw : N/A Current Power Limit : N/A Requested Power Limit : N/A Default Power Limit : N/A Min Power Limit : N/A Max Power Limit : N/A ```
GiteaMirror commented 2026-04-22 18:26:36 -05:00
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@rick-github commented on GitHub (Jan 4, 2026):

Setting 'stream:true' would return unusable data, something like this.

Yes, the suggestion was to check the timestamps to verify that token generation is slow. The snippet you posted shows 24 tokens over 1.96 seconds or ~12t/s. or approx 0.08538±0.01148 seconds per token. Based on the snippet the slowness is in token generation and not, eg ollama processing.

    Average Power Draw                : 48.31 W
    Instantaneous Power Draw          : 43.04 W

If the Spark is running inference at this point this power draw indicates underperformance. The GPU Current Temp is 81C for what is almost an idle GPU, and the Clocks Event Reasons Counters shows events for SW Power Capping and SW Thermal Slowdown. At a guess, I'd say the machine is overheating due to poor cooling and is throttling the clocks.

<!-- gh-comment-id:3707826891 --> @rick-github commented on GitHub (Jan 4, 2026): > Setting 'stream:true' would return unusable data, something like this. Yes, the suggestion was to check the timestamps to verify that token generation is slow. The snippet you posted shows 24 tokens over 1.96 seconds or ~12t/s. or approx 0.08538±0.01148 seconds per token. Based on the snippet the slowness is in token generation and not, eg ollama processing. > Average Power Draw : 48.31 W > Instantaneous Power Draw : 43.04 W If the Spark is running inference at this point this power draw indicates underperformance. The `GPU Current Temp` is 81C for what is almost an idle GPU, and the `Clocks Event Reasons Counters` shows events for `SW Power Capping` and `SW Thermal Slowdown`. At a guess, I'd say the machine is overheating due to poor cooling and is throttling the clocks.
GiteaMirror commented 2026-04-22 18:26:36 -05:00
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@Bcwz commented on GitHub (Jan 4, 2026):

If the Spark is running inference at this point this power draw indicates underperformance. The GPU Current Temp is 81C for what is almost an idle GPU, and the Clocks Event Reasons Counters shows events for SW Power Capping and SW Thermal Slowdown. At a guess, I'd say the machine is overheating due to poor cooling and is throttling the clocks.

The results from nvidia-smi -q -d POWER,TEMPERATURE,PERFORMANCE is not an idle GPU.

I'm currently running full load with multiple models.

Even on Nvidia forums - the temperature is

well within limits for a GPU under stress

This shows only less than a second for SW Thermal Slowdown, it does not explain the slowness/timeout for the prompts.

Clocks Event Reasons Counters
SW Power Capping : 165687 us
Sync Boost : 0 us
SW Thermal Slowdown : 903158 us
HW Thermal Slowdown : 0 us
HW Power Braking : 0 us

<!-- gh-comment-id:3707836083 --> @Bcwz commented on GitHub (Jan 4, 2026): > If the Spark is running inference at this point this power draw indicates underperformance. The `GPU Current Temp` is 81C for what is almost an idle GPU, and the `Clocks Event Reasons Counters` shows events for `SW Power Capping` and `SW Thermal Slowdown`. At a guess, I'd say the machine is overheating due to poor cooling and is throttling the clocks. The results from nvidia-smi -q -d POWER,TEMPERATURE,PERFORMANCE is not an idle GPU. I'm currently running full load with multiple models. Even on [Nvidia forums](https://forums.developer.nvidia.com/t/dgx-spark-thermal-throttling/349647/7) - the temperature is > well within limits for a GPU under stress This shows only less than a second for SW Thermal Slowdown, it does not explain the slowness/timeout for the prompts. > Clocks Event Reasons Counters > SW Power Capping : 165687 us > Sync Boost : 0 us > SW Thermal Slowdown : 903158 us > HW Thermal Slowdown : 0 us > HW Power Braking : 0 us >
GiteaMirror commented 2026-04-22 18:26:38 -05:00
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@rick-github commented on GitHub (Jan 4, 2026):

It's well within limits for a GPU under stress because the GPU is not under stress. The GPU should be drawing ~120W at full load. It is not. So your system is running slow because it is underperforming. Based on the evidence, it is running too hot. Unfortunately I don't have access to a DGX so I cannot offer any more advice.

<!-- gh-comment-id:3707871819 --> @rick-github commented on GitHub (Jan 4, 2026): It's well within limits for a GPU under stress because the GPU is not under stress. The GPU should be drawing ~120W at full load. It is not. So your system is running slow because it is underperforming. Based on the evidence, it is running too hot. Unfortunately I don't have access to a DGX so I cannot offer any more advice.
GiteaMirror commented 2026-04-22 18:26:39 -05:00
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@Bcwz commented on GitHub (Jan 4, 2026):

The GPU should be drawing ~120W at full load. It is not.

Yes, it should be capable of drawing more watts. I'm not limiting the power.

Based on the evidence, it is running too hot.

I disagree. The temperature is clearly within the expected range.

I can only draw the conclusion that Ollama is not optimized/ready for DGX Spark.

<!-- gh-comment-id:3707904735 --> @Bcwz commented on GitHub (Jan 4, 2026): >The GPU should be drawing ~120W at full load. It is not. Yes, it should be capable of drawing more watts. I'm not limiting the power. >Based on the evidence, it is running too hot. I disagree. The temperature is clearly within the expected range. I can only draw the conclusion that Ollama is not optimized/ready for DGX Spark.
GiteaMirror commented 2026-04-22 18:26:41 -05:00
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@rick-github commented on GitHub (Jan 4, 2026):

The temperature is within the expected range because the clocks are being throttled, reducing power draw and preventing the temperature from exceeding the limit.

<!-- gh-comment-id:3707907377 --> @rick-github commented on GitHub (Jan 4, 2026): The temperature is within the expected range because the clocks are being throttled, reducing power draw and preventing the temperature from exceeding the limit.
GiteaMirror commented 2026-04-22 18:26:43 -05:00
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@Bcwz commented on GitHub (Jan 14, 2026):

After some testing, i've observed that DGX Spark is capable of drawing more power even at higher temperature.

I've observed that it was capable of drawing more than double to around 80-ish Watts, even at higher temperature.

==============NVSMI LOG==============

Timestamp                                 : Thu Jan 15 02:04:13 2026
Driver Version                            : 580.95.05
CUDA Version                              : 13.0

Attached GPUs                             : 1
GPU 0000000F:01:00.0
    Performance State                     : P0
    Clocks Event Reasons
        Idle                              : Not Active
        Applications Clocks Setting       : Not Active
        SW Power Cap                      : Not Active
        HW Slowdown                       : Not Active
            HW Thermal Slowdown           : Not Active
            HW Power Brake Slowdown       : Not Active
        Sync Boost                        : Not Active
        SW Thermal Slowdown               : Not Active
        Display Clock Setting             : Not Active
    Clocks Event Reasons Counters
        SW Power Capping                  : 0 us
        Sync Boost                        : 0 us
        SW Thermal Slowdown               : 173453251 us
        HW Thermal Slowdown               : 403805 us
        HW Power Braking                  : 0 us
    Sparse Operation Mode                 : N/A
    Temperature
        GPU Current Temp                  : 84 C
        GPU T.Limit Temp                  : 6 C
        GPU Shutdown T.Limit Temp         : -5 C
        GPU Slowdown T.Limit Temp         : -2 C
        GPU Max Operating T.Limit Temp    : 0 C
        GPU Target Temperature            : N/A
        Memory Current Temp               : N/A
        Memory Max Operating T.Limit Temp : N/A
    GPU Power Readings
        Average Power Draw                : 80.64 W
        Instantaneous Power Draw          : 78.23 W
        Current Power Limit               : N/A
        Requested Power Limit             : N/A
        Default Power Limit               : N/A
        Min Power Limit                   : N/A
        Max Power Limit                   : N/A
    Power Samples
        Duration                          : Not Found
        Number of Samples                 : Not Found
        Max                               : Not Found
        Min                               : Not Found
        Avg                               : Not Found
    GPU Memory Power Readings 
        Average Power Draw                : N/A
        Instantaneous Power Draw          : N/A
    Module Power Readings
        Average Power Draw                : N/A
        Instantaneous Power Draw          : N/A
        Current Power Limit               : N/A
        Requested Power Limit             : N/A
        Default Power Limit               : N/A
        Min Power Limit                   : N/A
        Max Power Limit                   : N/A

So the previous assumption that it was being limited due to thermal throttle is false.

It seems to me that Ollama is not optimized to use the full potential of DGX Spark.

<!-- gh-comment-id:3750924290 --> @Bcwz commented on GitHub (Jan 14, 2026): After some testing, i've observed that DGX Spark is capable of drawing more power even at higher temperature. I've observed that it was capable of drawing more than double to around 80-ish Watts, even at higher temperature. ``` ==============NVSMI LOG============== Timestamp : Thu Jan 15 02:04:13 2026 Driver Version : 580.95.05 CUDA Version : 13.0 Attached GPUs : 1 GPU 0000000F:01:00.0 Performance State : P0 Clocks Event Reasons Idle : Not Active Applications Clocks Setting : Not Active SW Power Cap : Not Active HW Slowdown : Not Active HW Thermal Slowdown : Not Active HW Power Brake Slowdown : Not Active Sync Boost : Not Active SW Thermal Slowdown : Not Active Display Clock Setting : Not Active Clocks Event Reasons Counters SW Power Capping : 0 us Sync Boost : 0 us SW Thermal Slowdown : 173453251 us HW Thermal Slowdown : 403805 us HW Power Braking : 0 us Sparse Operation Mode : N/A Temperature GPU Current Temp : 84 C GPU T.Limit Temp : 6 C GPU Shutdown T.Limit Temp : -5 C GPU Slowdown T.Limit Temp : -2 C GPU Max Operating T.Limit Temp : 0 C GPU Target Temperature : N/A Memory Current Temp : N/A Memory Max Operating T.Limit Temp : N/A GPU Power Readings Average Power Draw : 80.64 W Instantaneous Power Draw : 78.23 W Current Power Limit : N/A Requested Power Limit : N/A Default Power Limit : N/A Min Power Limit : N/A Max Power Limit : N/A Power Samples Duration : Not Found Number of Samples : Not Found Max : Not Found Min : Not Found Avg : Not Found GPU Memory Power Readings Average Power Draw : N/A Instantaneous Power Draw : N/A Module Power Readings Average Power Draw : N/A Instantaneous Power Draw : N/A Current Power Limit : N/A Requested Power Limit : N/A Default Power Limit : N/A Min Power Limit : N/A Max Power Limit : N/A ``` So the previous assumption that it was being limited due to thermal throttle is false. It seems to me that Ollama is not optimized to use the full potential of DGX Spark.
GiteaMirror commented 2026-04-22 18:26:43 -05:00
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@rick-github commented on GitHub (Jan 15, 2026):

There are no other reports of performance issues with ollama on DGX Spark, so it's more likely a local problem. If GPU throttling is not the issue, the next thing to look at would be memory bandwidth. Are there any other processes running at the same time as ollama that might be actively reading/writing RAM? Does the system log report an memory errors? What;s the output of:

sudo hdparm -tT /dev/nvme0n1
<!-- gh-comment-id:3756045319 --> @rick-github commented on GitHub (Jan 15, 2026): There are no other reports of performance issues with ollama on DGX Spark, so it's more likely a local problem. If GPU throttling is not the issue, the next thing to look at would be memory bandwidth. Are there any other processes running at the same time as ollama that might be actively reading/writing RAM? Does the system log report an memory errors? What;s the output of: ``` sudo hdparm -tT /dev/nvme0n1 ```
GiteaMirror commented 2026-04-22 18:26:43 -05:00
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@Bcwz commented on GitHub (Jan 16, 2026):

sudo hdparm -tT /dev/nvme0n1

/dev/nvme0n1:
 Timing cached reads:   49666 MB in  1.99 seconds = 24903.53 MB/sec
 Timing buffered disk reads: 3854 MB in  3.00 seconds = 1284.36 MB/sec

This is currently running a full load

NAME            ID              SIZE     PROCESSOR    CONTEXT    UNTIL            
gpt-oss:120b    a951a23b46a1    73 GB    100% GPU     16000      6 hours from now    
gemma3:27b      a418f5838eaf    36 GB    100% GPU     16000      6 hours from now

There are independent reports/test that indicates performance issues Ollama.

https://forums.developer.nvidia.com/t/gdx-spark-is-extremely-slow-on-a-short-llm-test/350703/2
https://forums.developer.nvidia.com/t/very-poor-performance-with-ollama-on-dgx-spark-looking-for-help/353456

The general consensus seems to suggest that llama.cpp / vllm/ LM Studio tends to perform much better compared to Ollama.

<!-- gh-comment-id:3757697057 --> @Bcwz commented on GitHub (Jan 16, 2026): ``` sudo hdparm -tT /dev/nvme0n1 /dev/nvme0n1: Timing cached reads: 49666 MB in 1.99 seconds = 24903.53 MB/sec Timing buffered disk reads: 3854 MB in 3.00 seconds = 1284.36 MB/sec ``` This is currently running a full load ``` NAME ID SIZE PROCESSOR CONTEXT UNTIL gpt-oss:120b a951a23b46a1 73 GB 100% GPU 16000 6 hours from now gemma3:27b a418f5838eaf 36 GB 100% GPU 16000 6 hours from now ``` There are independent reports/test that indicates performance issues Ollama. [https://forums.developer.nvidia.com/t/gdx-spark-is-extremely-slow-on-a-short-llm-test/350703/2](url) [https://forums.developer.nvidia.com/t/very-poor-performance-with-ollama-on-dgx-spark-looking-for-help/353456](url) The general consensus seems to suggest that llama.cpp / vllm/ LM Studio tends to perform much better compared to Ollama.
GiteaMirror commented 2026-04-22 18:26:43 -05:00
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@Bcwz commented on GitHub (Jan 16, 2026):

Don't use Ollama.
Consider using other software instead.

<!-- gh-comment-id:3757705346 --> @Bcwz commented on GitHub (Jan 16, 2026): Don't use Ollama. Consider using other software instead.
GiteaMirror commented 2026-04-22 18:26:44 -05:00
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@Essence9999 commented on GitHub (Jan 16, 2026):

Don't use Ollama. Consider using other software instead.

which one? I am trying LLM by ollama on dgx spark

<!-- gh-comment-id:3758652436 --> @Essence9999 commented on GitHub (Jan 16, 2026): > Don't use Ollama. Consider using other software instead. which one? I am trying LLM by ollama on dgx spark
GiteaMirror commented 2026-04-22 18:26:45 -05:00
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@Bcwz commented on GitHub (Jan 16, 2026):

Don't use Ollama. Consider using other software instead.

which one? I am trying LLM by ollama on dgx spark

I cannot point to one specific software and say that it will give you the best performance.
Based on my research so far, most are recommending llama.cpp for it's fast performance.

I have yet to test any of the other software so do your own testing to see which suits your use case better.

<!-- gh-comment-id:3758670541 --> @Bcwz commented on GitHub (Jan 16, 2026): > > Don't use Ollama. Consider using other software instead. > > which one? I am trying LLM by ollama on dgx spark I cannot point to one specific software and say that it will give you the best performance. Based on my research so far, most are recommending [llama.cpp](https://github.com/ggml-org/llama.cpp) for it's fast performance. I have yet to test any of the other software so do your own testing to see which suits your use case better.
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