github-starred/ollama
2
0
Fork 0
mirror of https://github.com/ollama/ollama.git synced 2026-05-07 08:30:05 -05:00
Code Issues 14.5k Packages Projects Releases 120 Wiki Activity

[GH-ISSUE #15878] Windows Ollama 0.22.0 uses CPU only on AMD Ryzen AI 9 HX 470 / Radeon 890M #72176

New Issue
Open
opened 2026-05-05 03:35:42 -05:00 by GiteaMirror · 0 comments
GiteaMirror commented 2026-05-05 03:35:42 -05:00
Owner
Copy Link

Originally created by @henrytseuk-pixel on GitHub (Apr 29, 2026).
Original GitHub issue: https://github.com/ollama/ollama/issues/15878

What is the issue?

I am raising this after a clean reinstall and controlled test.

Hardware:

  • Minisforum AI X1 Pro-470
  • AMD Ryzen AI 9 HX 470
  • Integrated AMD Radeon 890M Graphics
  • 128GB RAM
  • Windows 11 Pro
  • UMA frame buffer previously tested at 16GB

GPU / driver:

  • AMD Radeon(TM) 890M Graphics
  • Driver version: 32.0.23033.1002
  • Driver date: 09/03/2026

Ollama:

  • Official Windows install
  • Ollama version: 0.22.0
  • Install path: C:\Users\hen_t\AppData\Local\Programs\Ollama\ollama.exe
  • API works on http://127.0.0.1:11434

Clean-up before reinstall:

  • Removed previous AMD AI Bundle Ollama
  • Removed WSL Ollama
  • Removed Docker Ollama image/container/volume
  • Confirmed no previous Ollama process, command, port 11434 listener, PATH entry, model store, or startup shortcut before reinstall

Test:

  1. Installed official Windows Ollama
  2. Ran: ollama pull llama3.2:1b
  3. Ran: ollama run llama3.2:1b
  4. Ran: ollama ps

Result:

  • Model runs successfully
  • ollama ps reports: 100% CPU
  • No GPU offload observed, even with small llama3.2:1b model

Observed process:

  • ollama.exe serve
  • ollama.exe
  • ollama.exe runner --model ...

Question:
Does official Windows Ollama currently support GPU acceleration on AMD Ryzen AI 9 HX 470 / Radeon 890M / gfx1150?
If not, is support planned?
If yes, what diagnostic logs or environment checks would help determine why Ollama falls back to CPU?

I can provide server.log, ollama ps output, GPU driver details

Relevant log output

time=2026-04-29T14:20:56.084+01:00 level=INFO source=app_windows.go:282 msg="starting Ollama" app=C:\Users\hen_t\AppData\Local\Programs\Ollama version=0.22.0 OS=Windows/10.0.26200
time=2026-04-29T14:20:56.088+01:00 level=INFO source=app.go:239 msg="initialized tools registry" tool_count=0
time=2026-04-29T14:20:56.088+01:00 level=INFO source=app.go:254 msg="starting ollama server"
time=2026-04-29T14:20:56.089+01:00 level=INFO source=app.go:285 msg="starting ui server" port=58389
time=2026-04-29T14:20:56.146+01:00 level=INFO source=app_windows.go:268 msg="Created Startup shortcut" shortcut="C:\\Users\\hen_t\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\Ollama.lnk"
time=2026-04-29T14:20:57.089+01:00 level=WARN source=server.go:237 msg="models path not accessible, using default" path=C:\Users\hen_t\.ollama\models err="CreateFile C:\\Users\\hen_t\\.ollama\\models: The system cannot find the path specified."
time=2026-04-29T14:20:59.089+01:00 level=INFO source=updater.go:296 msg="beginning update checker" interval=1h0m0s
time=2026-04-29T14:20:59.089+01:00 level=INFO source=auth.go:62 msg="Failed to load private key: open C:\\Users\\hen_t\\.ollama\\id_ed25519: The system cannot find the path specified."


Couldn't find 'C:\Users\hen_t\.ollama\id_ed25519'. Generating new private key.
Your new public key is: 

ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIJ3wiBzcD+J+7uwUsZJQw04F/337GWStdezGmRYYmwGI

time=2026-04-29T14:20:59.820+01:00 level=INFO source=routes.go:1762 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:0 OLLAMA_DEBUG:INFO OLLAMA_DEBUG_LOG_REQUESTS:false OLLAMA_EDITOR: OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 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:C:\\Users\\hen_t\\.ollama\\models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NO_CLOUD: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:]"
time=2026-04-29T14:20:59.820+01:00 level=INFO source=routes.go:1764 msg="Ollama cloud disabled: false"
time=2026-04-29T14:20:59.821+01:00 level=INFO source=images.go:517 msg="total blobs: 0"
time=2026-04-29T14:20:59.821+01:00 level=INFO source=images.go:524 msg="total unused blobs removed: 0"
time=2026-04-29T14:20:59.821+01:00 level=INFO source=routes.go:1820 msg="Listening on 127.0.0.1:11434 (version 0.22.0)"
time=2026-04-29T14:20:59.822+01:00 level=INFO source=runner.go:67 msg="discovering available GPUs..."
time=2026-04-29T14:20:59.834+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58744"
time=2026-04-29T14:21:27.391+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58754"
time=2026-04-29T14:21:34.953+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58766"
time=2026-04-29T14:21:42.792+01:00 level=INFO source=runner.go:106 msg="experimental Vulkan support disabled.  To enable, set OLLAMA_VULKAN=1"
time=2026-04-29T14:21:42.806+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58772"
time=2026-04-29T14:21:43.062+01:00 level=INFO source=types.go:60 msg="inference compute" id=cpu library=cpu compute="" name=cpu description=cpu libdirs=ollama driver="" pci_id="" type="" total="111.6 GiB" available="83.6 GiB"
time=2026-04-29T14:21:43.062+01:00 level=INFO source=routes.go:1870 msg="vram-based default context" total_vram="0 B" default_num_ctx=4096
[GIN] 2026/04/29 - 14:21:43 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:21:43 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:21:43 | 200 |      1.6079ms |       127.0.0.1 | GET      "/api/tags"
[GIN] 2026/04/29 - 14:21:43 | 200 |       529.3µs |       127.0.0.1 | GET      "/api/status"
[GIN] 2026/04/29 - 14:21:52 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:21:54 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:22:04 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:22:09 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:22:11 | 200 |            0s |       127.0.0.1 | HEAD     "/"
time=2026-04-29T14:22:12.304+01:00 level=INFO source=download.go:179 msg="downloading 74701a8c35f6 in 14 100 MB part(s)"
time=2026-04-29T14:22:39.213+01:00 level=INFO source=download.go:179 msg="downloading 966de95ca8a6 in 1 1.4 KB part(s)"
time=2026-04-29T14:22:40.514+01:00 level=INFO source=download.go:179 msg="downloading fcc5a6bec9da in 1 7.7 KB part(s)"
time=2026-04-29T14:22:41.815+01:00 level=INFO source=download.go:179 msg="downloading a70ff7e570d9 in 1 6.0 KB part(s)"
time=2026-04-29T14:22:43.128+01:00 level=INFO source=download.go:179 msg="downloading 4f659a1e86d7 in 1 485 B part(s)"
[GIN] 2026/04/29 - 14:22:46 | 200 |   35.2083808s |       127.0.0.1 | POST     "/api/pull"
[GIN] 2026/04/29 - 14:23:03 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:23:03 | 200 |    123.8675ms |       127.0.0.1 | POST     "/api/show"
time=2026-04-29T14:23:03.396+01:00 level=INFO source=cpu_windows.go:148 msg=packages count=1
time=2026-04-29T14:23:03.396+01:00 level=INFO source=cpu_windows.go:164 msg="efficiency cores detected" maxEfficiencyClass=1
time=2026-04-29T14:23:03.396+01:00 level=INFO source=cpu_windows.go:195 msg="" package=0 cores=12 efficiency=8 threads=24
llama_model_loader: loaded meta data with 30 key-value pairs and 147 tensors from C:\Users\hen_t\.ollama\models\blobs\sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 (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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Llama-3.2
llama_model_loader: - kv   5:                         general.size_label str              = 1B
llama_model_loader: - kv   6:                               general.tags arr[str,6]       = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv   7:                          general.languages arr[str,8]       = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv   8:                          llama.block_count u32              = 16
llama_model_loader: - kv   9:                       llama.context_length u32              = 131072
llama_model_loader: - kv  10:                     llama.embedding_length u32              = 2048
llama_model_loader: - kv  11:                  llama.feed_forward_length u32              = 8192
llama_model_loader: - kv  12:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  13:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  14:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  15:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  16:                 llama.attention.key_length u32              = 64
llama_model_loader: - kv  17:               llama.attention.value_length u32              = 64
llama_model_loader: - kv  18:                          general.file_type u32              = 7
llama_model_loader: - kv  19:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  20:                 llama.rope.dimension_count u32              = 64
llama_model_loader: - kv  21:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  22:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  23:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  24:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  25:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  26:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  27:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  28:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv  29:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   34 tensors
llama_model_loader: - type q8_0:  113 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 1.22 GiB (8.50 BPW) 
load: printing all EOG tokens:
load:   - 128001 ('<|end_of_text|>')
load:   - 128008 ('<|eom_id|>')
load:   - 128009 ('<|eot_id|>')
load: special tokens cache size = 256
load: token to piece cache size = 0.7999 MB
print_info: arch             = llama
print_info: vocab_only       = 1
print_info: no_alloc         = 0
print_info: model type       = ?B
print_info: model params     = 1.24 B
print_info: general.name     = Llama 3.2 1B Instruct
print_info: vocab type       = BPE
print_info: n_vocab          = 128256
print_info: n_merges         = 280147
print_info: BOS token        = 128000 '<|begin_of_text|>'
print_info: EOS token        = 128009 '<|eot_id|>'
print_info: EOT token        = 128001 '<|end_of_text|>'
print_info: EOM token        = 128008 '<|eom_id|>'
print_info: LF token         = 198 'Ċ'
print_info: EOG token        = 128001 '<|end_of_text|>'
print_info: EOG token        = 128008 '<|eom_id|>'
print_info: EOG token        = 128009 '<|eot_id|>'
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2026-04-29T14:23:03.886+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model C:\\Users\\hen_t\\.ollama\\models\\blobs\\sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 --port 63009"
time=2026-04-29T14:23:03.893+01:00 level=INFO source=sched.go:484 msg="system memory" total="111.6 GiB" free="84.5 GiB" free_swap="106.1 GiB"
time=2026-04-29T14:23:03.893+01:00 level=INFO source=server.go:511 msg="loading model" "model layers"=17 requested=-1
time=2026-04-29T14:23:03.893+01:00 level=INFO source=device.go:245 msg="model weights" device=CPU size="1.2 GiB"
time=2026-04-29T14:23:03.893+01:00 level=INFO source=device.go:256 msg="kv cache" device=CPU size="128.0 MiB"
time=2026-04-29T14:23:03.893+01:00 level=INFO source=device.go:272 msg="total memory" size="1.3 GiB"
time=2026-04-29T14:23:03.933+01:00 level=INFO source=runner.go:965 msg="starting go runner"
load_backend: loaded CPU backend from C:\Users\hen_t\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-icelake.dll
time=2026-04-29T14:23:03.947+01:00 level=INFO source=ggml.go:104 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.AVX512=1 CPU.0.AVX512_VBMI=1 CPU.0.AVX512_VNNI=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 compiler=cgo(clang)
time=2026-04-29T14:23:03.947+01:00 level=INFO source=runner.go:1001 msg="Server listening on 127.0.0.1:63009"
time=2026-04-29T14:23:03.952+01:00 level=INFO source=runner.go:895 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:Auto KvSize:4096 KvCacheType: NumThreads:4 GPULayers:[] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2026-04-29T14:23:03.952+01:00 level=INFO source=server.go:1364 msg="waiting for llama runner to start responding"
time=2026-04-29T14:23:03.952+01:00 level=INFO source=server.go:1398 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: loaded meta data with 30 key-value pairs and 147 tensors from C:\Users\hen_t\.ollama\models\blobs\sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 (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              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Llama-3.2
llama_model_loader: - kv   5:                         general.size_label str              = 1B
llama_model_loader: - kv   6:                               general.tags arr[str,6]       = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv   7:                          general.languages arr[str,8]       = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv   8:                          llama.block_count u32              = 16
llama_model_loader: - kv   9:                       llama.context_length u32              = 131072
llama_model_loader: - kv  10:                     llama.embedding_length u32              = 2048
llama_model_loader: - kv  11:                  llama.feed_forward_length u32              = 8192
llama_model_loader: - kv  12:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  13:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  14:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  15:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  16:                 llama.attention.key_length u32              = 64
llama_model_loader: - kv  17:               llama.attention.value_length u32              = 64
llama_model_loader: - kv  18:                          general.file_type u32              = 7
llama_model_loader: - kv  19:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  20:                 llama.rope.dimension_count u32              = 64
llama_model_loader: - kv  21:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  22:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  23:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  24:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  25:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  26:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  27:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  28:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv  29:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   34 tensors
llama_model_loader: - type q8_0:  113 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 1.22 GiB (8.50 BPW) 
load: printing all EOG tokens:
load:   - 128001 ('<|end_of_text|>')
load:   - 128008 ('<|eom_id|>')
load:   - 128009 ('<|eot_id|>')
load: special tokens cache size = 256
load: token to piece cache size = 0.7999 MB
print_info: arch             = llama
print_info: vocab_only       = 0
print_info: no_alloc         = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 2048
print_info: n_embd_inp       = 2048
print_info: n_layer          = 16
print_info: n_head           = 32
print_info: n_head_kv        = 8
print_info: n_rot            = 64
print_info: n_swa            = 0
print_info: is_swa_any       = 0
print_info: n_embd_head_k    = 64
print_info: n_embd_head_v    = 64
print_info: n_gqa            = 4
print_info: n_embd_k_gqa     = 512
print_info: n_embd_v_gqa     = 512
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             = 8192
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        = 0
print_info: rope scaling     = linear
print_info: freq_base_train  = 500000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn  = 131072
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned   = unknown
print_info: model type       = 1B
print_info: model params     = 1.24 B
print_info: general.name     = Llama 3.2 1B Instruct
print_info: vocab type       = BPE
print_info: n_vocab          = 128256
print_info: n_merges         = 280147
print_info: BOS token        = 128000 '<|begin_of_text|>'
print_info: EOS token        = 128009 '<|eot_id|>'
print_info: EOT token        = 128001 '<|end_of_text|>'
print_info: EOM token        = 128008 '<|eom_id|>'
print_info: LF token         = 198 'Ċ'
print_info: EOG token        = 128001 '<|end_of_text|>'
print_info: EOG token        = 128008 '<|eom_id|>'
print_info: EOG token        = 128009 '<|eot_id|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors:          CPU model buffer size =  1252.41 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    = auto
llama_context: kv_unified    = false
llama_context: freq_base     = 500000.0
llama_context: freq_scale    = 1
llama_context: n_ctx_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context:        CPU  output buffer size =     0.50 MiB
llama_kv_cache:        CPU KV buffer size =   128.00 MiB
llama_kv_cache: size =  128.00 MiB (  4096 cells,  16 layers,  1/1 seqs), K (f16):   64.00 MiB, V (f16):   64.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context:        CPU compute buffer size =   258.50 MiB
llama_context: graph nodes  = 503
llama_context: graph splits = 1
time=2026-04-29T14:23:04.454+01:00 level=INFO source=server.go:1402 msg="llama runner started in 0.56 seconds"
time=2026-04-29T14:23:04.455+01:00 level=INFO source=sched.go:561 msg="loaded runners" count=1
time=2026-04-29T14:23:04.455+01:00 level=INFO source=server.go:1364 msg="waiting for llama runner to start responding"
time=2026-04-29T14:23:04.455+01:00 level=INFO source=server.go:1402 msg="llama runner started in 0.56 seconds"
[GIN] 2026/04/29 - 14:23:08 | 200 |    5.6175667s |       127.0.0.1 | POST     "/api/generate"
[GIN] 2026/04/29 - 14:23:48 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:23:48 | 200 |            0s |       127.0.0.1 | GET      "/api/ps"
[GIN] 2026/04/29 - 14:24:03 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:24:03 | 200 |            0s |       127.0.0.1 | GET      "/api/ps"
[GIN] 2026/04/29 - 14:26:06 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:26:07 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:26:07 | 200 |            0s |       127.0.0.1 | GET      "/api/ps"
[GIN] 2026/04/29 - 14:28:21 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:28:22 | 200 |       506.4µs |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:28:22 | 200 |            0s |       127.0.0.1 | GET      "/api/ps"
[GIN] 2026/04/29 - 14:29:02 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:29:02 | 200 |            0s |       127.0.0.1 | GET      "/api/version"
[GIN] 2026/04/29 - 14:29:02 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:29:02 | 200 |      1.6282ms |       127.0.0.1 | GET      "/api/tags"
[GIN] 2026/04/29 - 14:29:02 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2026/04/29 - 14:29:02 | 200 |            0s |       127.0.0.1 | GET      "/api/ps"

OS

Windows

GPU

AMD

CPU

AMD

Ollama version

0.22.0

Originally created by @henrytseuk-pixel on GitHub (Apr 29, 2026). Original GitHub issue: https://github.com/ollama/ollama/issues/15878 ### What is the issue? I am raising this after a clean reinstall and controlled test. Hardware: - Minisforum AI X1 Pro-470 - AMD Ryzen AI 9 HX 470 - Integrated AMD Radeon 890M Graphics - 128GB RAM - Windows 11 Pro - UMA frame buffer previously tested at 16GB GPU / driver: - AMD Radeon(TM) 890M Graphics - Driver version: 32.0.23033.1002 - Driver date: 09/03/2026 Ollama: - Official Windows install - Ollama version: 0.22.0 - Install path: C:\Users\hen_t\AppData\Local\Programs\Ollama\ollama.exe - API works on http://127.0.0.1:11434 Clean-up before reinstall: - Removed previous AMD AI Bundle Ollama - Removed WSL Ollama - Removed Docker Ollama image/container/volume - Confirmed no previous Ollama process, command, port 11434 listener, PATH entry, model store, or startup shortcut before reinstall Test: 1. Installed official Windows Ollama 2. Ran: ollama pull llama3.2:1b 3. Ran: ollama run llama3.2:1b 4. Ran: ollama ps Result: - Model runs successfully - ollama ps reports: 100% CPU - No GPU offload observed, even with small llama3.2:1b model Observed process: - ollama.exe serve - ollama.exe - ollama.exe runner --model ... Question: Does official Windows Ollama currently support GPU acceleration on AMD Ryzen AI 9 HX 470 / Radeon 890M / gfx1150? If not, is support planned? If yes, what diagnostic logs or environment checks would help determine why Ollama falls back to CPU? I can provide server.log, ollama ps output, GPU driver details ### Relevant log output ```shell time=2026-04-29T14:20:56.084+01:00 level=INFO source=app_windows.go:282 msg="starting Ollama" app=C:\Users\hen_t\AppData\Local\Programs\Ollama version=0.22.0 OS=Windows/10.0.26200 time=2026-04-29T14:20:56.088+01:00 level=INFO source=app.go:239 msg="initialized tools registry" tool_count=0 time=2026-04-29T14:20:56.088+01:00 level=INFO source=app.go:254 msg="starting ollama server" time=2026-04-29T14:20:56.089+01:00 level=INFO source=app.go:285 msg="starting ui server" port=58389 time=2026-04-29T14:20:56.146+01:00 level=INFO source=app_windows.go:268 msg="Created Startup shortcut" shortcut="C:\\Users\\hen_t\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\Ollama.lnk" time=2026-04-29T14:20:57.089+01:00 level=WARN source=server.go:237 msg="models path not accessible, using default" path=C:\Users\hen_t\.ollama\models err="CreateFile C:\\Users\\hen_t\\.ollama\\models: The system cannot find the path specified." time=2026-04-29T14:20:59.089+01:00 level=INFO source=updater.go:296 msg="beginning update checker" interval=1h0m0s time=2026-04-29T14:20:59.089+01:00 level=INFO source=auth.go:62 msg="Failed to load private key: open C:\\Users\\hen_t\\.ollama\\id_ed25519: The system cannot find the path specified." Couldn't find 'C:\Users\hen_t\.ollama\id_ed25519'. Generating new private key. Your new public key is: ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIJ3wiBzcD+J+7uwUsZJQw04F/337GWStdezGmRYYmwGI time=2026-04-29T14:20:59.820+01:00 level=INFO source=routes.go:1762 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:0 OLLAMA_DEBUG:INFO OLLAMA_DEBUG_LOG_REQUESTS:false OLLAMA_EDITOR: OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 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:C:\\Users\\hen_t\\.ollama\\models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NO_CLOUD: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:]" time=2026-04-29T14:20:59.820+01:00 level=INFO source=routes.go:1764 msg="Ollama cloud disabled: false" time=2026-04-29T14:20:59.821+01:00 level=INFO source=images.go:517 msg="total blobs: 0" time=2026-04-29T14:20:59.821+01:00 level=INFO source=images.go:524 msg="total unused blobs removed: 0" time=2026-04-29T14:20:59.821+01:00 level=INFO source=routes.go:1820 msg="Listening on 127.0.0.1:11434 (version 0.22.0)" time=2026-04-29T14:20:59.822+01:00 level=INFO source=runner.go:67 msg="discovering available GPUs..." time=2026-04-29T14:20:59.834+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58744" time=2026-04-29T14:21:27.391+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58754" time=2026-04-29T14:21:34.953+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58766" time=2026-04-29T14:21:42.792+01:00 level=INFO source=runner.go:106 msg="experimental Vulkan support disabled. To enable, set OLLAMA_VULKAN=1" time=2026-04-29T14:21:42.806+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58772" time=2026-04-29T14:21:43.062+01:00 level=INFO source=types.go:60 msg="inference compute" id=cpu library=cpu compute="" name=cpu description=cpu libdirs=ollama driver="" pci_id="" type="" total="111.6 GiB" available="83.6 GiB" time=2026-04-29T14:21:43.062+01:00 level=INFO source=routes.go:1870 msg="vram-based default context" total_vram="0 B" default_num_ctx=4096 [GIN] 2026/04/29 - 14:21:43 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:21:43 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:21:43 | 200 | 1.6079ms | 127.0.0.1 | GET "/api/tags" [GIN] 2026/04/29 - 14:21:43 | 200 | 529.3µs | 127.0.0.1 | GET "/api/status" [GIN] 2026/04/29 - 14:21:52 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:21:54 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:22:04 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:22:09 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:22:11 | 200 | 0s | 127.0.0.1 | HEAD "/" time=2026-04-29T14:22:12.304+01:00 level=INFO source=download.go:179 msg="downloading 74701a8c35f6 in 14 100 MB part(s)" time=2026-04-29T14:22:39.213+01:00 level=INFO source=download.go:179 msg="downloading 966de95ca8a6 in 1 1.4 KB part(s)" time=2026-04-29T14:22:40.514+01:00 level=INFO source=download.go:179 msg="downloading fcc5a6bec9da in 1 7.7 KB part(s)" time=2026-04-29T14:22:41.815+01:00 level=INFO source=download.go:179 msg="downloading a70ff7e570d9 in 1 6.0 KB part(s)" time=2026-04-29T14:22:43.128+01:00 level=INFO source=download.go:179 msg="downloading 4f659a1e86d7 in 1 485 B part(s)" [GIN] 2026/04/29 - 14:22:46 | 200 | 35.2083808s | 127.0.0.1 | POST "/api/pull" [GIN] 2026/04/29 - 14:23:03 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:23:03 | 200 | 123.8675ms | 127.0.0.1 | POST "/api/show" time=2026-04-29T14:23:03.396+01:00 level=INFO source=cpu_windows.go:148 msg=packages count=1 time=2026-04-29T14:23:03.396+01:00 level=INFO source=cpu_windows.go:164 msg="efficiency cores detected" maxEfficiencyClass=1 time=2026-04-29T14:23:03.396+01:00 level=INFO source=cpu_windows.go:195 msg="" package=0 cores=12 efficiency=8 threads=24 llama_model_loader: loaded meta data with 30 key-value pairs and 147 tensors from C:\Users\hen_t\.ollama\models\blobs\sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 (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 = llama llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Llama 3.2 1B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Llama-3.2 llama_model_loader: - kv 5: general.size_label str = 1B llama_model_loader: - kv 6: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam... llama_model_loader: - kv 7: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ... llama_model_loader: - kv 8: llama.block_count u32 = 16 llama_model_loader: - kv 9: llama.context_length u32 = 131072 llama_model_loader: - kv 10: llama.embedding_length u32 = 2048 llama_model_loader: - kv 11: llama.feed_forward_length u32 = 8192 llama_model_loader: - kv 12: llama.attention.head_count u32 = 32 llama_model_loader: - kv 13: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 14: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 15: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 16: llama.attention.key_length u32 = 64 llama_model_loader: - kv 17: llama.attention.value_length u32 = 64 llama_model_loader: - kv 18: general.file_type u32 = 7 llama_model_loader: - kv 19: llama.vocab_size u32 = 128256 llama_model_loader: - kv 20: llama.rope.dimension_count u32 = 64 llama_model_loader: - kv 21: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 22: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 23: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 24: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 25: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 26: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 27: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 28: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ... llama_model_loader: - kv 29: general.quantization_version u32 = 2 llama_model_loader: - type f32: 34 tensors llama_model_loader: - type q8_0: 113 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q8_0 print_info: file size = 1.22 GiB (8.50 BPW) load: printing all EOG tokens: load: - 128001 ('<|end_of_text|>') load: - 128008 ('<|eom_id|>') load: - 128009 ('<|eot_id|>') load: special tokens cache size = 256 load: token to piece cache size = 0.7999 MB print_info: arch = llama print_info: vocab_only = 1 print_info: no_alloc = 0 print_info: model type = ?B print_info: model params = 1.24 B print_info: general.name = Llama 3.2 1B Instruct print_info: vocab type = BPE print_info: n_vocab = 128256 print_info: n_merges = 280147 print_info: BOS token = 128000 '<|begin_of_text|>' print_info: EOS token = 128009 '<|eot_id|>' print_info: EOT token = 128001 '<|end_of_text|>' print_info: EOM token = 128008 '<|eom_id|>' print_info: LF token = 198 'Ċ' print_info: EOG token = 128001 '<|end_of_text|>' print_info: EOG token = 128008 '<|eom_id|>' print_info: EOG token = 128009 '<|eot_id|>' print_info: max token length = 256 llama_model_load: vocab only - skipping tensors time=2026-04-29T14:23:03.886+01:00 level=INFO source=server.go:444 msg="starting runner" cmd="C:\\Users\\hen_t\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model C:\\Users\\hen_t\\.ollama\\models\\blobs\\sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 --port 63009" time=2026-04-29T14:23:03.893+01:00 level=INFO source=sched.go:484 msg="system memory" total="111.6 GiB" free="84.5 GiB" free_swap="106.1 GiB" time=2026-04-29T14:23:03.893+01:00 level=INFO source=server.go:511 msg="loading model" "model layers"=17 requested=-1 time=2026-04-29T14:23:03.893+01:00 level=INFO source=device.go:245 msg="model weights" device=CPU size="1.2 GiB" time=2026-04-29T14:23:03.893+01:00 level=INFO source=device.go:256 msg="kv cache" device=CPU size="128.0 MiB" time=2026-04-29T14:23:03.893+01:00 level=INFO source=device.go:272 msg="total memory" size="1.3 GiB" time=2026-04-29T14:23:03.933+01:00 level=INFO source=runner.go:965 msg="starting go runner" load_backend: loaded CPU backend from C:\Users\hen_t\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-icelake.dll time=2026-04-29T14:23:03.947+01:00 level=INFO source=ggml.go:104 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.AVX512=1 CPU.0.AVX512_VBMI=1 CPU.0.AVX512_VNNI=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 compiler=cgo(clang) time=2026-04-29T14:23:03.947+01:00 level=INFO source=runner.go:1001 msg="Server listening on 127.0.0.1:63009" time=2026-04-29T14:23:03.952+01:00 level=INFO source=runner.go:895 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:Auto KvSize:4096 KvCacheType: NumThreads:4 GPULayers:[] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}" time=2026-04-29T14:23:03.952+01:00 level=INFO source=server.go:1364 msg="waiting for llama runner to start responding" time=2026-04-29T14:23:03.952+01:00 level=INFO source=server.go:1398 msg="waiting for server to become available" status="llm server loading model" llama_model_loader: loaded meta data with 30 key-value pairs and 147 tensors from C:\Users\hen_t\.ollama\models\blobs\sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 (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 = llama llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Llama 3.2 1B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Llama-3.2 llama_model_loader: - kv 5: general.size_label str = 1B llama_model_loader: - kv 6: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam... llama_model_loader: - kv 7: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ... llama_model_loader: - kv 8: llama.block_count u32 = 16 llama_model_loader: - kv 9: llama.context_length u32 = 131072 llama_model_loader: - kv 10: llama.embedding_length u32 = 2048 llama_model_loader: - kv 11: llama.feed_forward_length u32 = 8192 llama_model_loader: - kv 12: llama.attention.head_count u32 = 32 llama_model_loader: - kv 13: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 14: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 15: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 16: llama.attention.key_length u32 = 64 llama_model_loader: - kv 17: llama.attention.value_length u32 = 64 llama_model_loader: - kv 18: general.file_type u32 = 7 llama_model_loader: - kv 19: llama.vocab_size u32 = 128256 llama_model_loader: - kv 20: llama.rope.dimension_count u32 = 64 llama_model_loader: - kv 21: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 22: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 23: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 24: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 25: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 26: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 27: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 28: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ... llama_model_loader: - kv 29: general.quantization_version u32 = 2 llama_model_loader: - type f32: 34 tensors llama_model_loader: - type q8_0: 113 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q8_0 print_info: file size = 1.22 GiB (8.50 BPW) load: printing all EOG tokens: load: - 128001 ('<|end_of_text|>') load: - 128008 ('<|eom_id|>') load: - 128009 ('<|eot_id|>') load: special tokens cache size = 256 load: token to piece cache size = 0.7999 MB print_info: arch = llama print_info: vocab_only = 0 print_info: no_alloc = 0 print_info: n_ctx_train = 131072 print_info: n_embd = 2048 print_info: n_embd_inp = 2048 print_info: n_layer = 16 print_info: n_head = 32 print_info: n_head_kv = 8 print_info: n_rot = 64 print_info: n_swa = 0 print_info: is_swa_any = 0 print_info: n_embd_head_k = 64 print_info: n_embd_head_v = 64 print_info: n_gqa = 4 print_info: n_embd_k_gqa = 512 print_info: n_embd_v_gqa = 512 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 = 8192 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 = 0 print_info: rope scaling = linear print_info: freq_base_train = 500000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 131072 print_info: rope_yarn_log_mul= 0.0000 print_info: rope_finetuned = unknown print_info: model type = 1B print_info: model params = 1.24 B print_info: general.name = Llama 3.2 1B Instruct print_info: vocab type = BPE print_info: n_vocab = 128256 print_info: n_merges = 280147 print_info: BOS token = 128000 '<|begin_of_text|>' print_info: EOS token = 128009 '<|eot_id|>' print_info: EOT token = 128001 '<|end_of_text|>' print_info: EOM token = 128008 '<|eom_id|>' print_info: LF token = 198 'Ċ' print_info: EOG token = 128001 '<|end_of_text|>' print_info: EOG token = 128008 '<|eom_id|>' print_info: EOG token = 128009 '<|eot_id|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = false) load_tensors: CPU model buffer size = 1252.41 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 = auto llama_context: kv_unified = false llama_context: freq_base = 500000.0 llama_context: freq_scale = 1 llama_context: n_ctx_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized llama_context: CPU output buffer size = 0.50 MiB llama_kv_cache: CPU KV buffer size = 128.00 MiB llama_kv_cache: size = 128.00 MiB ( 4096 cells, 16 layers, 1/1 seqs), K (f16): 64.00 MiB, V (f16): 64.00 MiB llama_context: Flash Attention was auto, set to enabled llama_context: CPU compute buffer size = 258.50 MiB llama_context: graph nodes = 503 llama_context: graph splits = 1 time=2026-04-29T14:23:04.454+01:00 level=INFO source=server.go:1402 msg="llama runner started in 0.56 seconds" time=2026-04-29T14:23:04.455+01:00 level=INFO source=sched.go:561 msg="loaded runners" count=1 time=2026-04-29T14:23:04.455+01:00 level=INFO source=server.go:1364 msg="waiting for llama runner to start responding" time=2026-04-29T14:23:04.455+01:00 level=INFO source=server.go:1402 msg="llama runner started in 0.56 seconds" [GIN] 2026/04/29 - 14:23:08 | 200 | 5.6175667s | 127.0.0.1 | POST "/api/generate" [GIN] 2026/04/29 - 14:23:48 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:23:48 | 200 | 0s | 127.0.0.1 | GET "/api/ps" [GIN] 2026/04/29 - 14:24:03 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:24:03 | 200 | 0s | 127.0.0.1 | GET "/api/ps" [GIN] 2026/04/29 - 14:26:06 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:26:07 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:26:07 | 200 | 0s | 127.0.0.1 | GET "/api/ps" [GIN] 2026/04/29 - 14:28:21 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:28:22 | 200 | 506.4µs | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:28:22 | 200 | 0s | 127.0.0.1 | GET "/api/ps" [GIN] 2026/04/29 - 14:29:02 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:29:02 | 200 | 0s | 127.0.0.1 | GET "/api/version" [GIN] 2026/04/29 - 14:29:02 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:29:02 | 200 | 1.6282ms | 127.0.0.1 | GET "/api/tags" [GIN] 2026/04/29 - 14:29:02 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2026/04/29 - 14:29:02 | 200 | 0s | 127.0.0.1 | GET "/api/ps" ``` ### OS Windows ### GPU AMD ### CPU AMD ### Ollama version 0.22.0
GiteaMirror added the bug label 2026-05-05 03:35:42 -05:00
Sign in to join this conversation.
No Branch/Tag Specified
Branches Tags
Labels
Clear labels
amd
api
app
bug
build
cli
cloud
compatibility
context-length
create
docker
documentation
embeddings
feature request
feedback wanted
good first issue
gpt-oss
gpu
harmony
help wanted
image
install
intel
js
launch
linux
macos
memory
mlx
model
needs more info
networking
nvidia
ollama.com
performance
pull-request
Mirrored from GitHub Pull Request
 python
question
registry
rendering
thinking
tools
top
vulkan
windows
wsl
No Label bug
Milestone
No items
No Milestone
Projects
Clear projects
No project
Assignees
Clear assignees
GiteaMirror ninjasurge
No Assignees
1 Participants
Notifications
Due Date
No due date set.
Dependencies

No dependencies set.

Reference: github-starred/ollama#72176
Delete Branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?