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[GH-ISSUE #10476] OLLAMA_NUM_THREAD is ignored in version 0.6.6 #6890

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opened 2026-04-12 18:45:42 -05:00 by GiteaMirror · 3 comments
GiteaMirror commented 2026-04-12 18:45:42 -05:00
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Originally created by @Jerrynicki on GitHub (Apr 29, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/10476

What is the issue?

After upgrading ollama to 0.6.6 from 0.6.5, only 2 CPU threads are being utilized instead of the 4 threads specified in the OLLAMA_NUM_THREAD environment variable.
Image

This affects all models I've tested.

ollama.service file:

[Unit]
Description=Ollama Service
After=network-online.target

[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
Environment="OLLAMA_NUM_THREAD=4"
Environment="OLLAMA_HOST=127.0.0.1:10000"
Environment="OLLAMA_MAX_LOADED_MODELS=1"
Environment="OLLAMA_NUM_PARALLEL=1"
Environment="OLLAMA_KEEP_ALIVE=24h"
Environment="OLLAMA_FLASH_ATTENTION=1"

[Install]
WantedBy=default.target

Using options: {"num_thread": 4} in an API requests also results in only 2 CPU threads being utilized.

Weirdly, it does work correctly in the CLI after using /set parameter num_thread 4.

The log shows llama server only being started with 2 threads. ollama[530410]: time=2025-04-29T16:36:35.462+02:00 level=INFO source=server.go:405 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 --ctx-size 4096 --batch-size 512 --threads 2 --no-mmap --parallel 1 --port 33935"

Also just launching with the environment variable gives the same problem. I've attached the full log where I launch ollama with the relevant env vars and run ollama run qwen3:4b

Relevant log output

OLLAMA_HOST=127.0.0.1:10000 OLLAMA_NUM_THREAD=4 /usr/local/bin/ollama serve
Couldn't find '/root/.ollama/id_ed25519'. Generating new private key.
Your new public key is: 

ssh-ed25519 [removed]

2025/04/29 16:47:03 routes.go:1232: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:2048 OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:10000 OLLAMA_INTEL_GPU:false 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:0 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_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
time=2025-04-29T16:47:03.856+02:00 level=INFO source=images.go:458 msg="total blobs: 0"
time=2025-04-29T16:47:03.856+02:00 level=INFO source=images.go:465 msg="total unused blobs removed: 0"
time=2025-04-29T16:47:03.856+02:00 level=INFO source=routes.go:1299 msg="Listening on 127.0.0.1:10000 (version 0.6.6)"
time=2025-04-29T16:47:03.857+02:00 level=INFO source=gpu.go:217 msg="looking for compatible GPUs"
time=2025-04-29T16:47:03.863+02:00 level=INFO source=gpu.go:377 msg="no compatible GPUs were discovered"
time=2025-04-29T16:47:03.863+02:00 level=INFO source=types.go:130 msg="inference compute" id=0 library=cpu variant="" compute="" driver=0.0 name="" total="7.7 GiB" available="6.3 GiB"
[removed logs from downloading the model]
time=2025-04-29T16:47:33.440+02:00 level=INFO source=server.go:105 msg="system memory" total="7.7 GiB" free="6.3 GiB" free_swap="11.8 GiB"
time=2025-04-29T16:47:33.440+02:00 level=WARN source=ggml.go:152 msg="key not found" key=qwen3.vision.block_count default=0
time=2025-04-29T16:47:33.440+02:00 level=INFO source=server.go:138 msg=offload library=cpu layers.requested=-1 layers.model=37 layers.offload=0 layers.split="" memory.available="[6.3 GiB]" memory.gpu_overhead="0 B" memory.required.full="4.4 GiB" memory.required.partial="0 B" memory.required.kv="1.1 GiB" memory.required.allocations="[4.4 GiB]" memory.weights.total="2.4 GiB" memory.weights.repeating="2.1 GiB" memory.weights.nonrepeating="304.3 MiB" memory.graph.full="768.0 MiB" memory.graph.partial="768.0 MiB"
llama_model_loader: loaded meta data with 27 key-value pairs and 398 tensors from /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 (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              = qwen3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3 4B
llama_model_loader: - kv   3:                           general.basename str              = Qwen3
llama_model_loader: - kv   4:                         general.size_label str              = 4B
llama_model_loader: - kv   5:                          qwen3.block_count u32              = 36
llama_model_loader: - kv   6:                       qwen3.context_length u32              = 40960
llama_model_loader: - kv   7:                     qwen3.embedding_length u32              = 2560
llama_model_loader: - kv   8:                  qwen3.feed_forward_length u32              = 9728
llama_model_loader: - kv   9:                 qwen3.attention.head_count u32              = 32
llama_model_loader: - kv  10:              qwen3.attention.head_count_kv u32              = 8
llama_model_loader: - kv  11:                       qwen3.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  12:     qwen3.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  13:                 qwen3.attention.key_length u32              = 128
llama_model_loader: - kv  14:               qwen3.attention.value_length u32              = 128
llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  16:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  17:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  19:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  20:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  21:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  22:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  23:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  24:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  25:               general.quantization_version u32              = 2
llama_model_loader: - kv  26:                          general.file_type u32              = 15
llama_model_loader: - type  f32:  145 tensors
llama_model_loader: - type  f16:   36 tensors
llama_model_loader: - type q4_K:  198 tensors
llama_model_loader: - type q6_K:   19 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 2.44 GiB (5.20 BPW) 
load: special tokens cache size = 26
load: token to piece cache size = 0.9311 MB
print_info: arch             = qwen3
print_info: vocab_only       = 1
print_info: model type       = ?B
print_info: model params     = 4.02 B
print_info: general.name     = Qwen3 4B
print_info: vocab type       = BPE
print_info: n_vocab          = 151936
print_info: n_merges         = 151387
print_info: BOS token        = 151643 '<|endoftext|>'
print_info: EOS token        = 151645 '<|im_end|>'
print_info: EOT token        = 151645 '<|im_end|>'
print_info: PAD token        = 151643 '<|endoftext|>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
print_info: FIM MID token    = 151660 '<|fim_middle|>'
print_info: FIM PAD token    = 151662 '<|fim_pad|>'
print_info: FIM REP token    = 151663 '<|repo_name|>'
print_info: FIM SEP token    = 151664 '<|file_sep|>'
print_info: EOG token        = 151643 '<|endoftext|>'
print_info: EOG token        = 151645 '<|im_end|>'
print_info: EOG token        = 151662 '<|fim_pad|>'
print_info: EOG token        = 151663 '<|repo_name|>'
print_info: EOG token        = 151664 '<|file_sep|>'
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2025-04-29T16:47:33.689+02:00 level=INFO source=server.go:405 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 --ctx-size 8192 --batch-size 512 --threads 2 --no-mmap --parallel 4 --port 41599"
time=2025-04-29T16:47:33.689+02:00 level=INFO source=sched.go:451 msg="loaded runners" count=1
time=2025-04-29T16:47:33.689+02:00 level=INFO source=server.go:580 msg="waiting for llama runner to start responding"
time=2025-04-29T16:47:33.689+02:00 level=INFO source=server.go:614 msg="waiting for server to become available" status="llm server error"
time=2025-04-29T16:47:33.703+02:00 level=INFO source=runner.go:853 msg="starting go runner"
load_backend: loaded CPU backend from /usr/local/lib/ollama/libggml-cpu-haswell.so
time=2025-04-29T16:47:33.709+02:00 level=INFO source=ggml.go:109 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 compiler=cgo(gcc)
time=2025-04-29T16:47:33.709+02:00 level=INFO source=runner.go:913 msg="Server listening on 127.0.0.1:41599"
llama_model_loader: loaded meta data with 27 key-value pairs and 398 tensors from /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 (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              = qwen3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3 4B
llama_model_loader: - kv   3:                           general.basename str              = Qwen3
llama_model_loader: - kv   4:                         general.size_label str              = 4B
llama_model_loader: - kv   5:                          qwen3.block_count u32              = 36
llama_model_loader: - kv   6:                       qwen3.context_length u32              = 40960
llama_model_loader: - kv   7:                     qwen3.embedding_length u32              = 2560
llama_model_loader: - kv   8:                  qwen3.feed_forward_length u32              = 9728
llama_model_loader: - kv   9:                 qwen3.attention.head_count u32              = 32
llama_model_loader: - kv  10:              qwen3.attention.head_count_kv u32              = 8
llama_model_loader: - kv  11:                       qwen3.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  12:     qwen3.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  13:                 qwen3.attention.key_length u32              = 128
llama_model_loader: - kv  14:               qwen3.attention.value_length u32              = 128
llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  16:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  17:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  19:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  20:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  21:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  22:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  23:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  24:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  25:               general.quantization_version u32              = 2
llama_model_loader: - kv  26:                          general.file_type u32              = 15
llama_model_loader: - type  f32:  145 tensors
llama_model_loader: - type  f16:   36 tensors
llama_model_loader: - type q4_K:  198 tensors
llama_model_loader: - type q6_K:   19 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 2.44 GiB (5.20 BPW) 
load: special tokens cache size = 26
time=2025-04-29T16:47:33.940+02:00 level=INFO source=server.go:614 msg="waiting for server to become available" status="llm server loading model"
load: token to piece cache size = 0.9311 MB
print_info: arch             = qwen3
print_info: vocab_only       = 0
print_info: n_ctx_train      = 40960
print_info: n_embd           = 2560
print_info: n_layer          = 36
print_info: n_head           = 32
print_info: n_head_kv        = 8
print_info: n_rot            = 128
print_info: n_swa            = 0
print_info: n_swa_pattern    = 1
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     = 1024
print_info: n_embd_v_gqa     = 1024
print_info: f_norm_eps       = 0.0e+00
print_info: f_norm_rms_eps   = 1.0e-06
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             = 9728
print_info: n_expert         = 0
print_info: n_expert_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  = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn  = 40960
print_info: rope_finetuned   = unknown
print_info: ssm_d_conv       = 0
print_info: ssm_d_inner      = 0
print_info: ssm_d_state      = 0
print_info: ssm_dt_rank      = 0
print_info: ssm_dt_b_c_rms   = 0
print_info: model type       = ?B
print_info: model params     = 4.02 B
print_info: general.name     = Qwen3 4B
print_info: vocab type       = BPE
print_info: n_vocab          = 151936
print_info: n_merges         = 151387
print_info: BOS token        = 151643 '<|endoftext|>'
print_info: EOS token        = 151645 '<|im_end|>'
print_info: EOT token        = 151645 '<|im_end|>'
print_info: PAD token        = 151643 '<|endoftext|>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
print_info: FIM MID token    = 151660 '<|fim_middle|>'
print_info: FIM PAD token    = 151662 '<|fim_pad|>'
print_info: FIM REP token    = 151663 '<|repo_name|>'
print_info: FIM SEP token    = 151664 '<|file_sep|>'
print_info: EOG token        = 151643 '<|endoftext|>'
print_info: EOG token        = 151645 '<|im_end|>'
print_info: EOG token        = 151662 '<|fim_pad|>'
print_info: EOG token        = 151663 '<|repo_name|>'
print_info: EOG token        = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors:          CPU model buffer size =  2493.69 MiB
llama_context: constructing llama_context
llama_context: n_seq_max     = 4
llama_context: n_ctx         = 8192
llama_context: n_ctx_per_seq = 2048
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = 0
llama_context: freq_base     = 1000000.0
llama_context: freq_scale    = 1
llama_context: n_ctx_per_seq (2048) < n_ctx_train (40960) -- the full capacity of the model will not be utilized
llama_context:        CPU  output buffer size =     2.36 MiB
init: kv_size = 8192, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 36, can_shift = 1
init:        CPU KV buffer size =  1152.00 MiB
llama_context: KV self size  = 1152.00 MiB, K (f16):  576.00 MiB, V (f16):  576.00 MiB
llama_context:        CPU compute buffer size =   554.01 MiB
llama_context: graph nodes  = 1374
llama_context: graph splits = 1
time=2025-04-29T16:47:37.964+02:00 level=INFO source=server.go:619 msg="llama runner started in 4.27 seconds"
[GIN] 2025/04/29 - 16:47:37 | 200 |  4.605695083s |       127.0.0.1 | POST     "/api/generate"
time=2025-04-29T16:47:46.264+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32
[GIN] 2025/04/29 - 16:48:01 | 200 | 14.789974671s |       127.0.0.1 | POST     "/api/chat"

OS

Linux

GPU

No response

CPU

AMD

Ollama version

0.6.6

Originally created by @Jerrynicki on GitHub (Apr 29, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/10476 ### What is the issue? After upgrading ollama to 0.6.6 from 0.6.5, only 2 CPU threads are being utilized instead of the 4 threads specified in the OLLAMA_NUM_THREAD environment variable. ![Image](https://github.com/user-attachments/assets/18e63771-2a26-440f-93ac-fa1fbb3416e9) This affects all models I've tested. ollama.service file: ``` [Unit] Description=Ollama Service After=network-online.target [Service] ExecStart=/usr/local/bin/ollama serve User=ollama Group=ollama Restart=always RestartSec=3 Environment="PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin" Environment="OLLAMA_NUM_THREAD=4" Environment="OLLAMA_HOST=127.0.0.1:10000" Environment="OLLAMA_MAX_LOADED_MODELS=1" Environment="OLLAMA_NUM_PARALLEL=1" Environment="OLLAMA_KEEP_ALIVE=24h" Environment="OLLAMA_FLASH_ATTENTION=1" [Install] WantedBy=default.target ``` Using `options: {"num_thread": 4}` in an API requests also results in only 2 CPU threads being utilized. Weirdly, it does work correctly in the CLI after using `/set parameter num_thread 4`. The log shows llama server only being started with 2 threads. ``` ollama[530410]: time=2025-04-29T16:36:35.462+02:00 level=INFO source=server.go:405 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 --ctx-size 4096 --batch-size 512 --threads 2 --no-mmap --parallel 1 --port 33935"``` Also just launching with the environment variable gives the same problem. I've attached the full log where I launch ollama with the relevant env vars and run `ollama run qwen3:4b` ### Relevant log output ```shell OLLAMA_HOST=127.0.0.1:10000 OLLAMA_NUM_THREAD=4 /usr/local/bin/ollama serve Couldn't find '/root/.ollama/id_ed25519'. Generating new private key. Your new public key is: ssh-ed25519 [removed] 2025/04/29 16:47:03 routes.go:1232: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:2048 OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:10000 OLLAMA_INTEL_GPU:false 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:0 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_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]" time=2025-04-29T16:47:03.856+02:00 level=INFO source=images.go:458 msg="total blobs: 0" time=2025-04-29T16:47:03.856+02:00 level=INFO source=images.go:465 msg="total unused blobs removed: 0" time=2025-04-29T16:47:03.856+02:00 level=INFO source=routes.go:1299 msg="Listening on 127.0.0.1:10000 (version 0.6.6)" time=2025-04-29T16:47:03.857+02:00 level=INFO source=gpu.go:217 msg="looking for compatible GPUs" time=2025-04-29T16:47:03.863+02:00 level=INFO source=gpu.go:377 msg="no compatible GPUs were discovered" time=2025-04-29T16:47:03.863+02:00 level=INFO source=types.go:130 msg="inference compute" id=0 library=cpu variant="" compute="" driver=0.0 name="" total="7.7 GiB" available="6.3 GiB" [removed logs from downloading the model] time=2025-04-29T16:47:33.440+02:00 level=INFO source=server.go:105 msg="system memory" total="7.7 GiB" free="6.3 GiB" free_swap="11.8 GiB" time=2025-04-29T16:47:33.440+02:00 level=WARN source=ggml.go:152 msg="key not found" key=qwen3.vision.block_count default=0 time=2025-04-29T16:47:33.440+02:00 level=INFO source=server.go:138 msg=offload library=cpu layers.requested=-1 layers.model=37 layers.offload=0 layers.split="" memory.available="[6.3 GiB]" memory.gpu_overhead="0 B" memory.required.full="4.4 GiB" memory.required.partial="0 B" memory.required.kv="1.1 GiB" memory.required.allocations="[4.4 GiB]" memory.weights.total="2.4 GiB" memory.weights.repeating="2.1 GiB" memory.weights.nonrepeating="304.3 MiB" memory.graph.full="768.0 MiB" memory.graph.partial="768.0 MiB" llama_model_loader: loaded meta data with 27 key-value pairs and 398 tensors from /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 (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 = qwen3 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3 4B llama_model_loader: - kv 3: general.basename str = Qwen3 llama_model_loader: - kv 4: general.size_label str = 4B llama_model_loader: - kv 5: qwen3.block_count u32 = 36 llama_model_loader: - kv 6: qwen3.context_length u32 = 40960 llama_model_loader: - kv 7: qwen3.embedding_length u32 = 2560 llama_model_loader: - kv 8: qwen3.feed_forward_length u32 = 9728 llama_model_loader: - kv 9: qwen3.attention.head_count u32 = 32 llama_model_loader: - kv 10: qwen3.attention.head_count_kv u32 = 8 llama_model_loader: - kv 11: qwen3.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 12: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 13: qwen3.attention.key_length u32 = 128 llama_model_loader: - kv 14: qwen3.attention.value_length u32 = 128 llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 16: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 24: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 25: general.quantization_version u32 = 2 llama_model_loader: - kv 26: general.file_type u32 = 15 llama_model_loader: - type f32: 145 tensors llama_model_loader: - type f16: 36 tensors llama_model_loader: - type q4_K: 198 tensors llama_model_loader: - type q6_K: 19 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 2.44 GiB (5.20 BPW) load: special tokens cache size = 26 load: token to piece cache size = 0.9311 MB print_info: arch = qwen3 print_info: vocab_only = 1 print_info: model type = ?B print_info: model params = 4.02 B print_info: general.name = Qwen3 4B print_info: vocab type = BPE print_info: n_vocab = 151936 print_info: n_merges = 151387 print_info: BOS token = 151643 '<|endoftext|>' print_info: EOS token = 151645 '<|im_end|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151643 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 llama_model_load: vocab only - skipping tensors time=2025-04-29T16:47:33.689+02:00 level=INFO source=server.go:405 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 --ctx-size 8192 --batch-size 512 --threads 2 --no-mmap --parallel 4 --port 41599" time=2025-04-29T16:47:33.689+02:00 level=INFO source=sched.go:451 msg="loaded runners" count=1 time=2025-04-29T16:47:33.689+02:00 level=INFO source=server.go:580 msg="waiting for llama runner to start responding" time=2025-04-29T16:47:33.689+02:00 level=INFO source=server.go:614 msg="waiting for server to become available" status="llm server error" time=2025-04-29T16:47:33.703+02:00 level=INFO source=runner.go:853 msg="starting go runner" load_backend: loaded CPU backend from /usr/local/lib/ollama/libggml-cpu-haswell.so time=2025-04-29T16:47:33.709+02:00 level=INFO source=ggml.go:109 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 compiler=cgo(gcc) time=2025-04-29T16:47:33.709+02:00 level=INFO source=runner.go:913 msg="Server listening on 127.0.0.1:41599" llama_model_loader: loaded meta data with 27 key-value pairs and 398 tensors from /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 (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 = qwen3 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3 4B llama_model_loader: - kv 3: general.basename str = Qwen3 llama_model_loader: - kv 4: general.size_label str = 4B llama_model_loader: - kv 5: qwen3.block_count u32 = 36 llama_model_loader: - kv 6: qwen3.context_length u32 = 40960 llama_model_loader: - kv 7: qwen3.embedding_length u32 = 2560 llama_model_loader: - kv 8: qwen3.feed_forward_length u32 = 9728 llama_model_loader: - kv 9: qwen3.attention.head_count u32 = 32 llama_model_loader: - kv 10: qwen3.attention.head_count_kv u32 = 8 llama_model_loader: - kv 11: qwen3.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 12: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 13: qwen3.attention.key_length u32 = 128 llama_model_loader: - kv 14: qwen3.attention.value_length u32 = 128 llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 16: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 24: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 25: general.quantization_version u32 = 2 llama_model_loader: - kv 26: general.file_type u32 = 15 llama_model_loader: - type f32: 145 tensors llama_model_loader: - type f16: 36 tensors llama_model_loader: - type q4_K: 198 tensors llama_model_loader: - type q6_K: 19 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 2.44 GiB (5.20 BPW) load: special tokens cache size = 26 time=2025-04-29T16:47:33.940+02:00 level=INFO source=server.go:614 msg="waiting for server to become available" status="llm server loading model" load: token to piece cache size = 0.9311 MB print_info: arch = qwen3 print_info: vocab_only = 0 print_info: n_ctx_train = 40960 print_info: n_embd = 2560 print_info: n_layer = 36 print_info: n_head = 32 print_info: n_head_kv = 8 print_info: n_rot = 128 print_info: n_swa = 0 print_info: n_swa_pattern = 1 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 = 1024 print_info: n_embd_v_gqa = 1024 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-06 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 = 9728 print_info: n_expert = 0 print_info: n_expert_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 = 1000000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 40960 print_info: rope_finetuned = unknown print_info: ssm_d_conv = 0 print_info: ssm_d_inner = 0 print_info: ssm_d_state = 0 print_info: ssm_dt_rank = 0 print_info: ssm_dt_b_c_rms = 0 print_info: model type = ?B print_info: model params = 4.02 B print_info: general.name = Qwen3 4B print_info: vocab type = BPE print_info: n_vocab = 151936 print_info: n_merges = 151387 print_info: BOS token = 151643 '<|endoftext|>' print_info: EOS token = 151645 '<|im_end|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151643 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = false) load_tensors: CPU model buffer size = 2493.69 MiB llama_context: constructing llama_context llama_context: n_seq_max = 4 llama_context: n_ctx = 8192 llama_context: n_ctx_per_seq = 2048 llama_context: n_batch = 2048 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = 0 llama_context: freq_base = 1000000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (2048) < n_ctx_train (40960) -- the full capacity of the model will not be utilized llama_context: CPU output buffer size = 2.36 MiB init: kv_size = 8192, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 36, can_shift = 1 init: CPU KV buffer size = 1152.00 MiB llama_context: KV self size = 1152.00 MiB, K (f16): 576.00 MiB, V (f16): 576.00 MiB llama_context: CPU compute buffer size = 554.01 MiB llama_context: graph nodes = 1374 llama_context: graph splits = 1 time=2025-04-29T16:47:37.964+02:00 level=INFO source=server.go:619 msg="llama runner started in 4.27 seconds" [GIN] 2025/04/29 - 16:47:37 | 200 | 4.605695083s | 127.0.0.1 | POST "/api/generate" time=2025-04-29T16:47:46.264+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32 [GIN] 2025/04/29 - 16:48:01 | 200 | 14.789974671s | 127.0.0.1 | POST "/api/chat" ``` ### OS Linux ### GPU _No response_ ### CPU AMD ### Ollama version 0.6.6
GiteaMirror added the bug label 2026-04-12 18:45:42 -05:00
GiteaMirror commented 2026-04-12 18:45:44 -05:00
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@Jerrynicki commented on GitHub (Apr 29, 2025):

Update: using a custom Modelfile where PARAMETER num_thread 4 is specified makes ollama utilize 4 threads.

<!-- gh-comment-id:2839276350 --> @Jerrynicki commented on GitHub (Apr 29, 2025): Update: using a custom Modelfile where `PARAMETER num_thread 4` is specified makes ollama utilize 4 threads.
GiteaMirror commented 2026-04-12 18:45:44 -05:00
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@rick-github commented on GitHub (Apr 29, 2025):

OLLAMA_NUM_THREAD is not an ollama configuration variable.

Setting num_thread works here:

$ curl -s localhost:11434/api/generate -d '{"model":"qwen3:4b-q4_K_M","options":{"num_thread":4}}' | jq
{
  "model": "qwen3:4b-q4_K_M",
  "created_at": "2025-04-29T15:05:57.484125192Z",
  "response": "",
  "done": true,
  "done_reason": "load"
}
$ ps wwho cmd p$(pidof ollama)
/bin/ollama serve
/usr/bin/ollama runner --model /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 --ctx-size 2048 --batch-size 512 --n-gpu-layers 37 --verbose --threads 4 --parallel 1 --port 32961
<!-- gh-comment-id:2839276714 --> @rick-github commented on GitHub (Apr 29, 2025): `OLLAMA_NUM_THREAD` is not an ollama configuration variable. Setting `num_thread` works here: ```console $ curl -s localhost:11434/api/generate -d '{"model":"qwen3:4b-q4_K_M","options":{"num_thread":4}}' | jq { "model": "qwen3:4b-q4_K_M", "created_at": "2025-04-29T15:05:57.484125192Z", "response": "", "done": true, "done_reason": "load" } $ ps wwho cmd p$(pidof ollama) /bin/ollama serve /usr/bin/ollama runner --model /root/.ollama/models/blobs/sha256-163553aea1b1de62de7c5eb2ef5afb756b4b3133308d9ae7e42e951d8d696ef5 --ctx-size 2048 --batch-size 512 --n-gpu-layers 37 --verbose --threads 4 --parallel 1 --port 32961 ```
GiteaMirror commented 2026-04-12 18:45:45 -05:00
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@Jerrynicki commented on GitHub (Apr 29, 2025):

Huh, I might have just gotten confused then. Thank you for the quick reply!

<!-- gh-comment-id:2839282620 --> @Jerrynicki commented on GitHub (Apr 29, 2025): Huh, I might have just gotten confused then. Thank you for the quick reply!
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