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[GH-ISSUE #12634] errors: unable to allocate CUDA0 buffer | cudaMalloc failed: out of memory #54899

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opened 2026-04-29 07:54:58 -05:00 by GiteaMirror · 1 comment
GiteaMirror commented 2026-04-29 07:54:58 -05:00
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Originally created by @lencic on GitHub (Oct 15, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/12634

What is the issue?

When trying to run qwen2.5-coder:14b, deepseek-r1:14b, errors above occurs. Smaller models work fine. But i sure that i have enough VRAM to run qwen2.5-coder:14b, deepseek-r1:14b properly.

Relevant log output

time=2025-10-15T14:42:41.846+03:00 level=INFO source=routes.go:1481 msg="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:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:30m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:2 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:D:\\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 ROCR_VISIBLE_DEVICES:]"
time=2025-10-15T14:42:41.873+03:00 level=INFO source=images.go:522 msg="total blobs: 25"
time=2025-10-15T14:42:41.875+03:00 level=INFO source=images.go:529 msg="total unused blobs removed: 0"
time=2025-10-15T14:42:41.876+03:00 level=INFO source=routes.go:1534 msg="Listening on 127.0.0.1:11434 (version 0.12.5)"
time=2025-10-15T14:42:41.877+03:00 level=INFO source=runner.go:80 msg="discovering available GPUs..."
time=2025-10-15T14:42:43.552+03:00 level=INFO source=types.go:112 msg="inference compute" id=GPU-00651bdf-1c36-2569-996b-0e04d3021ca0 library=CUDA compute=12.0 name=CUDA0 description="NVIDIA GeForce RTX 5060 Ti" libdirs=ollama,cuda_v13 driver=13.0 pci_id=06:00.0 type=discrete total="15.9 GiB" available="14.4 GiB"
time=2025-10-15T14:42:43.552+03:00 level=INFO source=routes.go:1575 msg="entering low vram mode" "total vram"="15.9 GiB" threshold="20.0 GiB"
[GIN] 2025/10/15 - 14:42:43 | 200 |            0s |       127.0.0.1 | HEAD     "/"
[GIN] 2025/10/15 - 14:42:43 | 200 |     44.5636ms |       127.0.0.1 | POST     "/api/show"
llama_model_loader: loaded meta data with 34 key-value pairs and 579 tensors from D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed (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              = qwen2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen2.5 Coder 14B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Qwen2.5-Coder
llama_model_loader: - kv   5:                         general.size_label str              = 14B
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                       general.license.link str              = https://huggingface.co/Qwen/Qwen2.5-C...
llama_model_loader: - kv   8:                   general.base_model.count u32              = 1
llama_model_loader: - kv   9:                  general.base_model.0.name str              = Qwen2.5 Coder 14B
llama_model_loader: - kv  10:          general.base_model.0.organization str              = Qwen
llama_model_loader: - kv  11:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen2.5-C...
llama_model_loader: - kv  12:                               general.tags arr[str,6]       = ["code", "codeqwen", "chat", "qwen", ...
llama_model_loader: - kv  13:                          general.languages arr[str,1]       = ["en"]
llama_model_loader: - kv  14:                          qwen2.block_count u32              = 48
llama_model_loader: - kv  15:                       qwen2.context_length u32              = 32768
llama_model_loader: - kv  16:                     qwen2.embedding_length u32              = 5120
llama_model_loader: - kv  17:                  qwen2.feed_forward_length u32              = 13824
llama_model_loader: - kv  18:                 qwen2.attention.head_count u32              = 40
llama_model_loader: - kv  19:              qwen2.attention.head_count_kv u32              = 8
llama_model_loader: - kv  20:                       qwen2.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  21:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  22:                          general.file_type u32              = 15
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,152064]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,152064]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  29:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  33:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  241 tensors
llama_model_loader: - type q4_K:  289 tensors
llama_model_loader: - type q6_K:   49 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 8.37 GiB (4.87 BPW) 
load: printing all EOG tokens:
load:   - 151643 ('<|endoftext|>')
load:   - 151645 ('<|im_end|>')
load:   - 151662 ('<|fim_pad|>')
load:   - 151663 ('<|repo_name|>')
load:   - 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch             = qwen2
print_info: vocab_only       = 1
print_info: model type       = ?B
print_info: model params     = 14.77 B
print_info: general.name     = Qwen2.5 Coder 14B Instruct
print_info: vocab type       = BPE
print_info: n_vocab          = 152064
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=2025-10-15T14:42:44.032+03:00 level=INFO source=cpu_windows.go:139 msg=packages count=1
time=2025-10-15T14:42:44.032+03:00 level=INFO source=cpu_windows.go:186 msg="" package=0 cores=6 efficiency=0 threads=12
time=2025-10-15T14:42:44.033+03:00 level=INFO source=server.go:400 msg="starting runner" cmd="C:\\Users\\lenya\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model D:\\Ollama_Models\\blobs\\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed --port 62915"
time=2025-10-15T14:42:44.035+03:00 level=INFO source=cpu_windows.go:139 msg=packages count=1
time=2025-10-15T14:42:44.035+03:00 level=INFO source=cpu_windows.go:186 msg="" package=0 cores=6 efficiency=0 threads=12
time=2025-10-15T14:42:44.035+03:00 level=INFO source=server.go:505 msg="system memory" total="15.9 GiB" free="10.7 GiB" free_swap="8.9 GiB"
time=2025-10-15T14:42:44.036+03:00 level=INFO source=memory.go:36 msg="new model will fit in available VRAM across minimum required GPUs, loading" model=D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed library=CUDA parallel=1 required="9.7 GiB" gpus=1
time=2025-10-15T14:42:44.036+03:00 level=INFO source=server.go:545 msg=offload library=CUDA layers.requested=-1 layers.model=49 layers.offload=49 layers.split=[49] memory.available="[14.4 GiB]" memory.gpu_overhead="0 B" memory.required.full="9.7 GiB" memory.required.partial="9.7 GiB" memory.required.kv="768.0 MiB" memory.required.allocations="[9.7 GiB]" memory.weights.total="8.0 GiB" memory.weights.repeating="7.4 GiB" memory.weights.nonrepeating="609.1 MiB" memory.graph.full="348.0 MiB" memory.graph.partial="916.1 MiB"
time=2025-10-15T14:42:44.059+03:00 level=INFO source=runner.go:864 msg="starting go runner"
load_backend: loaded CPU backend from C:\Users\lenya\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll
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 GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, ID: GPU-00651bdf-1c36-2569-996b-0e04d3021ca0
load_backend: loaded CUDA backend from C:\Users\lenya\AppData\Local\Programs\Ollama\lib\ollama\cuda_v13\ggml-cuda.dll
time=2025-10-15T14:42:44.166+03:00 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.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2025-10-15T14:42:44.167+03:00 level=INFO source=runner.go:900 msg="Server listening on 127.0.0.1:62915"
time=2025-10-15T14:42:44.174+03:00 level=INFO source=runner.go:799 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:false KvSize:4096 KvCacheType: NumThreads:6 GPULayers:49[ID:GPU-00651bdf-1c36-2569-996b-0e04d3021ca0 Layers:49(0..48)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 5060 Ti) (0000:06:00.0) - 0 MiB free
time=2025-10-15T14:42:44.174+03:00 level=INFO source=server.go:1271 msg="waiting for llama runner to start responding"
time=2025-10-15T14:42:44.174+03:00 level=INFO source=server.go:1305 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: loaded meta data with 34 key-value pairs and 579 tensors from D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed (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              = qwen2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen2.5 Coder 14B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Qwen2.5-Coder
llama_model_loader: - kv   5:                         general.size_label str              = 14B
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                       general.license.link str              = https://huggingface.co/Qwen/Qwen2.5-C...
llama_model_loader: - kv   8:                   general.base_model.count u32              = 1
llama_model_loader: - kv   9:                  general.base_model.0.name str              = Qwen2.5 Coder 14B
llama_model_loader: - kv  10:          general.base_model.0.organization str              = Qwen
llama_model_loader: - kv  11:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen2.5-C...
llama_model_loader: - kv  12:                               general.tags arr[str,6]       = ["code", "codeqwen", "chat", "qwen", ...
llama_model_loader: - kv  13:                          general.languages arr[str,1]       = ["en"]
llama_model_loader: - kv  14:                          qwen2.block_count u32              = 48
llama_model_loader: - kv  15:                       qwen2.context_length u32              = 32768
llama_model_loader: - kv  16:                     qwen2.embedding_length u32              = 5120
llama_model_loader: - kv  17:                  qwen2.feed_forward_length u32              = 13824
llama_model_loader: - kv  18:                 qwen2.attention.head_count u32              = 40
llama_model_loader: - kv  19:              qwen2.attention.head_count_kv u32              = 8
llama_model_loader: - kv  20:                       qwen2.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  21:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  22:                          general.file_type u32              = 15
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,152064]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,152064]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  29:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  31:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  32:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  33:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  241 tensors
llama_model_loader: - type q4_K:  289 tensors
llama_model_loader: - type q6_K:   49 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 8.37 GiB (4.87 BPW) 
load: printing all EOG tokens:
load:   - 151643 ('<|endoftext|>')
load:   - 151645 ('<|im_end|>')
load:   - 151662 ('<|fim_pad|>')
load:   - 151663 ('<|repo_name|>')
load:   - 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch             = qwen2
print_info: vocab_only       = 0
print_info: n_ctx_train      = 32768
print_info: n_embd           = 5120
print_info: n_layer          = 48
print_info: n_head           = 40
print_info: n_head_kv        = 8
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            = 5
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             = 13824
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: causal attn      = 1
print_info: pooling type     = -1
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  = 32768
print_info: rope_finetuned   = unknown
print_info: model type       = 14B
print_info: model params     = 14.77 B
print_info: general.name     = Qwen2.5 Coder 14B Instruct
print_info: vocab type       = BPE
print_info: n_vocab          = 152064
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)
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 437944320
alloc_tensor_range: failed to allocate CPU buffer of size 437944320
llama_model_load: error loading model: unable to allocate CPU buffer
llama_model_load_from_file_impl: failed to load model
panic: unable to load model: D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed

goroutine 40 [running]:
github.com/ollama/ollama/runner/llamarunner.(*Server).loadModel(0xc00009be00, {0x31, 0x0, 0x0, {0xc00038ae54, 0x1, 0x1}, 0xc000712f10, 0x0}, {0xc000136000, ...}, ...)
	C:/a/ollama/ollama/runner/llamarunner/runner.go:747 +0x35f
created by github.com/ollama/ollama/runner/llamarunner.(*Server).load in goroutine 10
	C:/a/ollama/ollama/runner/llamarunner/runner.go:833 +0x7ce
time=2025-10-15T14:42:44.636+03:00 level=ERROR source=server.go:426 msg="llama runner terminated" error="exit status 2"
time=2025-10-15T14:42:44.727+03:00 level=INFO source=sched.go:449 msg="Load failed" model=D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed error="llama runner process has terminated: error loading model: unable to allocate CPU buffer"
[GIN] 2025/10/15 - 14:42:44 | 500 |    1.1272911s |       127.0.0.1 | POST     "/api/generate"

OS

Windows

GPU

Nvidia

CPU

AMD

Ollama version

0.12.5

Originally created by @lencic on GitHub (Oct 15, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/12634 ### What is the issue? When trying to run qwen2.5-coder:14b, deepseek-r1:14b, errors above occurs. Smaller models work fine. But i sure that i have enough VRAM to run qwen2.5-coder:14b, deepseek-r1:14b properly. ### Relevant log output ```shell time=2025-10-15T14:42:41.846+03:00 level=INFO source=routes.go:1481 msg="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:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:30m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:2 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:D:\\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 ROCR_VISIBLE_DEVICES:]" time=2025-10-15T14:42:41.873+03:00 level=INFO source=images.go:522 msg="total blobs: 25" time=2025-10-15T14:42:41.875+03:00 level=INFO source=images.go:529 msg="total unused blobs removed: 0" time=2025-10-15T14:42:41.876+03:00 level=INFO source=routes.go:1534 msg="Listening on 127.0.0.1:11434 (version 0.12.5)" time=2025-10-15T14:42:41.877+03:00 level=INFO source=runner.go:80 msg="discovering available GPUs..." time=2025-10-15T14:42:43.552+03:00 level=INFO source=types.go:112 msg="inference compute" id=GPU-00651bdf-1c36-2569-996b-0e04d3021ca0 library=CUDA compute=12.0 name=CUDA0 description="NVIDIA GeForce RTX 5060 Ti" libdirs=ollama,cuda_v13 driver=13.0 pci_id=06:00.0 type=discrete total="15.9 GiB" available="14.4 GiB" time=2025-10-15T14:42:43.552+03:00 level=INFO source=routes.go:1575 msg="entering low vram mode" "total vram"="15.9 GiB" threshold="20.0 GiB" [GIN] 2025/10/15 - 14:42:43 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2025/10/15 - 14:42:43 | 200 | 44.5636ms | 127.0.0.1 | POST "/api/show" llama_model_loader: loaded meta data with 34 key-value pairs and 579 tensors from D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed (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 = qwen2 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen2.5 Coder 14B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen2.5-Coder llama_model_loader: - kv 5: general.size_label str = 14B llama_model_loader: - kv 6: general.license str = apache-2.0 llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-C... llama_model_loader: - kv 8: general.base_model.count u32 = 1 llama_model_loader: - kv 9: general.base_model.0.name str = Qwen2.5 Coder 14B llama_model_loader: - kv 10: general.base_model.0.organization str = Qwen llama_model_loader: - kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-C... llama_model_loader: - kv 12: general.tags arr[str,6] = ["code", "codeqwen", "chat", "qwen", ... llama_model_loader: - kv 13: general.languages arr[str,1] = ["en"] llama_model_loader: - kv 14: qwen2.block_count u32 = 48 llama_model_loader: - kv 15: qwen2.context_length u32 = 32768 llama_model_loader: - kv 16: qwen2.embedding_length u32 = 5120 llama_model_loader: - kv 17: qwen2.feed_forward_length u32 = 13824 llama_model_loader: - kv 18: qwen2.attention.head_count u32 = 40 llama_model_loader: - kv 19: qwen2.attention.head_count_kv u32 = 8 llama_model_loader: - kv 20: qwen2.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 22: general.file_type u32 = 15 llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 24: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 30: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 31: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 33: general.quantization_version u32 = 2 llama_model_loader: - type f32: 241 tensors llama_model_loader: - type q4_K: 289 tensors llama_model_loader: - type q6_K: 49 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 8.37 GiB (4.87 BPW) load: printing all EOG tokens: load: - 151643 ('<|endoftext|>') load: - 151645 ('<|im_end|>') load: - 151662 ('<|fim_pad|>') load: - 151663 ('<|repo_name|>') load: - 151664 ('<|file_sep|>') load: special tokens cache size = 22 load: token to piece cache size = 0.9310 MB print_info: arch = qwen2 print_info: vocab_only = 1 print_info: model type = ?B print_info: model params = 14.77 B print_info: general.name = Qwen2.5 Coder 14B Instruct print_info: vocab type = BPE print_info: n_vocab = 152064 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=2025-10-15T14:42:44.032+03:00 level=INFO source=cpu_windows.go:139 msg=packages count=1 time=2025-10-15T14:42:44.032+03:00 level=INFO source=cpu_windows.go:186 msg="" package=0 cores=6 efficiency=0 threads=12 time=2025-10-15T14:42:44.033+03:00 level=INFO source=server.go:400 msg="starting runner" cmd="C:\\Users\\lenya\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model D:\\Ollama_Models\\blobs\\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed --port 62915" time=2025-10-15T14:42:44.035+03:00 level=INFO source=cpu_windows.go:139 msg=packages count=1 time=2025-10-15T14:42:44.035+03:00 level=INFO source=cpu_windows.go:186 msg="" package=0 cores=6 efficiency=0 threads=12 time=2025-10-15T14:42:44.035+03:00 level=INFO source=server.go:505 msg="system memory" total="15.9 GiB" free="10.7 GiB" free_swap="8.9 GiB" time=2025-10-15T14:42:44.036+03:00 level=INFO source=memory.go:36 msg="new model will fit in available VRAM across minimum required GPUs, loading" model=D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed library=CUDA parallel=1 required="9.7 GiB" gpus=1 time=2025-10-15T14:42:44.036+03:00 level=INFO source=server.go:545 msg=offload library=CUDA layers.requested=-1 layers.model=49 layers.offload=49 layers.split=[49] memory.available="[14.4 GiB]" memory.gpu_overhead="0 B" memory.required.full="9.7 GiB" memory.required.partial="9.7 GiB" memory.required.kv="768.0 MiB" memory.required.allocations="[9.7 GiB]" memory.weights.total="8.0 GiB" memory.weights.repeating="7.4 GiB" memory.weights.nonrepeating="609.1 MiB" memory.graph.full="348.0 MiB" memory.graph.partial="916.1 MiB" time=2025-10-15T14:42:44.059+03:00 level=INFO source=runner.go:864 msg="starting go runner" load_backend: loaded CPU backend from C:\Users\lenya\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll 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 GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, ID: GPU-00651bdf-1c36-2569-996b-0e04d3021ca0 load_backend: loaded CUDA backend from C:\Users\lenya\AppData\Local\Programs\Ollama\lib\ollama\cuda_v13\ggml-cuda.dll time=2025-10-15T14:42:44.166+03:00 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.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang) time=2025-10-15T14:42:44.167+03:00 level=INFO source=runner.go:900 msg="Server listening on 127.0.0.1:62915" time=2025-10-15T14:42:44.174+03:00 level=INFO source=runner.go:799 msg=load request="{Operation:commit LoraPath:[] Parallel:1 BatchSize:512 FlashAttention:false KvSize:4096 KvCacheType: NumThreads:6 GPULayers:49[ID:GPU-00651bdf-1c36-2569-996b-0e04d3021ca0 Layers:49(0..48)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}" llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 5060 Ti) (0000:06:00.0) - 0 MiB free time=2025-10-15T14:42:44.174+03:00 level=INFO source=server.go:1271 msg="waiting for llama runner to start responding" time=2025-10-15T14:42:44.174+03:00 level=INFO source=server.go:1305 msg="waiting for server to become available" status="llm server loading model" llama_model_loader: loaded meta data with 34 key-value pairs and 579 tensors from D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed (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 = qwen2 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen2.5 Coder 14B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen2.5-Coder llama_model_loader: - kv 5: general.size_label str = 14B llama_model_loader: - kv 6: general.license str = apache-2.0 llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-C... llama_model_loader: - kv 8: general.base_model.count u32 = 1 llama_model_loader: - kv 9: general.base_model.0.name str = Qwen2.5 Coder 14B llama_model_loader: - kv 10: general.base_model.0.organization str = Qwen llama_model_loader: - kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-C... llama_model_loader: - kv 12: general.tags arr[str,6] = ["code", "codeqwen", "chat", "qwen", ... llama_model_loader: - kv 13: general.languages arr[str,1] = ["en"] llama_model_loader: - kv 14: qwen2.block_count u32 = 48 llama_model_loader: - kv 15: qwen2.context_length u32 = 32768 llama_model_loader: - kv 16: qwen2.embedding_length u32 = 5120 llama_model_loader: - kv 17: qwen2.feed_forward_length u32 = 13824 llama_model_loader: - kv 18: qwen2.attention.head_count u32 = 40 llama_model_loader: - kv 19: qwen2.attention.head_count_kv u32 = 8 llama_model_loader: - kv 20: qwen2.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 22: general.file_type u32 = 15 llama_model_loader: - kv 23: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 24: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 30: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 31: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 33: general.quantization_version u32 = 2 llama_model_loader: - type f32: 241 tensors llama_model_loader: - type q4_K: 289 tensors llama_model_loader: - type q6_K: 49 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 8.37 GiB (4.87 BPW) load: printing all EOG tokens: load: - 151643 ('<|endoftext|>') load: - 151645 ('<|im_end|>') load: - 151662 ('<|fim_pad|>') load: - 151663 ('<|repo_name|>') load: - 151664 ('<|file_sep|>') load: special tokens cache size = 22 load: token to piece cache size = 0.9310 MB print_info: arch = qwen2 print_info: vocab_only = 0 print_info: n_ctx_train = 32768 print_info: n_embd = 5120 print_info: n_layer = 48 print_info: n_head = 40 print_info: n_head_kv = 8 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 = 5 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 = 13824 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: causal attn = 1 print_info: pooling type = -1 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 = 32768 print_info: rope_finetuned = unknown print_info: model type = 14B print_info: model params = 14.77 B print_info: general.name = Qwen2.5 Coder 14B Instruct print_info: vocab type = BPE print_info: n_vocab = 152064 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) ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 437944320 alloc_tensor_range: failed to allocate CPU buffer of size 437944320 llama_model_load: error loading model: unable to allocate CPU buffer llama_model_load_from_file_impl: failed to load model panic: unable to load model: D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed goroutine 40 [running]: github.com/ollama/ollama/runner/llamarunner.(*Server).loadModel(0xc00009be00, {0x31, 0x0, 0x0, {0xc00038ae54, 0x1, 0x1}, 0xc000712f10, 0x0}, {0xc000136000, ...}, ...) C:/a/ollama/ollama/runner/llamarunner/runner.go:747 +0x35f created by github.com/ollama/ollama/runner/llamarunner.(*Server).load in goroutine 10 C:/a/ollama/ollama/runner/llamarunner/runner.go:833 +0x7ce time=2025-10-15T14:42:44.636+03:00 level=ERROR source=server.go:426 msg="llama runner terminated" error="exit status 2" time=2025-10-15T14:42:44.727+03:00 level=INFO source=sched.go:449 msg="Load failed" model=D:\Ollama_Models\blobs\sha256-ac9bc7a69dab38da1c790838955f1293420b55ab555ef6b4615efa1c1507b1ed error="llama runner process has terminated: error loading model: unable to allocate CPU buffer" [GIN] 2025/10/15 - 14:42:44 | 500 | 1.1272911s | 127.0.0.1 | POST "/api/generate" ``` ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.12.5
GiteaMirror added the bug label 2026-04-29 07:54:58 -05:00
GiteaMirror commented 2026-04-29 07:55:03 -05:00
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@lencic commented on GitHub (Oct 15, 2025):

So, apparently ollama was in conflict with the installed nvidia CUDA tooklit. I deleted it and everything worked

<!-- gh-comment-id:3407574948 --> @lencic commented on GitHub (Oct 15, 2025): So, apparently ollama was in conflict with the installed nvidia CUDA tooklit. I deleted it and everything worked
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Reference: github-starred/ollama#54899
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