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[GH-ISSUE #3430] CUBLAS_STATUS_ALLOC_FAILED #2114

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opened 2026-04-12 12:21:08 -05:00 by GiteaMirror · 3 comments
GiteaMirror commented 2026-04-12 12:21:08 -05:00
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Originally created by @nanshaws on GitHub (Apr 1, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/3430

What is the issue?

time=2024-04-01T08:23:20.161+08:00 level=INFO source=gpu.go:115 msg="Detecting GPU type"
time=2024-04-01T08:23:20.161+08:00 level=INFO source=gpu.go:265 msg="Searching for GPU management library cudart64_*.dll"
time=2024-04-01T08:23:20.170+08:00 level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [C:\Users\Administrator\AppData\Local\Programs\Ollama\cudart64_110.dll c:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin\cudart64_110.dll C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin\cudart64_110.dll]"
time=2024-04-01T08:23:20.234+08:00 level=INFO source=gpu.go:120 msg="Nvidia GPU detected via cudart"
time=2024-04-01T08:23:20.235+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-01T08:23:20.382+08:00 level=INFO source=gpu.go:188 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6"
time=2024-04-01T08:23:20.382+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-01T08:23:20.382+08:00 level=INFO source=gpu.go:188 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6"
time=2024-04-01T08:23:20.383+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-01T08:23:20.383+08:00 level=INFO source=assets.go:108 msg="Updating PATH to C:\Users\ADMINI1\AppData\Local\Temp\ollama1513712000\runners\cuda_v11.3;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp;C:\Program Files (x86)\jdk/bin;D:\work\graalvm-jdk-17_windows-x64_bin\graalvm-jdk-17.0.9+11.1\bin;D:\WindowsVSC\VC\Tools\MSVC\14.36.32532\bin\Hostx64\x64\;C:\Program Files\PlasticSCM5\server;C:\Program Files\PlasticSCM5\client;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;D:\work\apache-tomcat-9.0.1-windows-x64\apache-tomcat-9.0.1\bin\;D:\work\apache-maven-3.8.8-bin\apache-maven-3.8.8\bin\;D:\work\gradle-8.2.1-all\gradle-8.2.1\bin;D:\work\apache-jmeter-5.5\bin;D:\work\w64devkit-1.19.0\w64devkit\bin;C:\Program Files\Docker\Docker\resources\bin;C:\Program Files\MySQL\MySQL Server 8.0\bin;D:\Git\cmd;D:\python\;D:\nvm;C:\Program Files\nodejs;D:\work\visualvm_216\bin;D:\HashiCorp\Vagrant\bin;D:\weixin\微信web开发者工具\dll;D:\work\netcat-win32-1.12;D:\work\VMware-ovftool-4.5.0-20459872-win.x86_64\ovftool;D:\work\lu;D:\work\kotlin-compiler-1.9.22\kotlinc\bin;C:\Program Files\CMake\bin;C:\Program Files\NVIDIA Corporation\Nsight Compute 2020.3.0\;D:\miniconda3;D:\miniconda3\Library\mingw-w64\bin;D:\miniconda3\Library\usr\bin;D:\miniconda3\Library\bin;D:\miniconda3\Scripts;C:\Program Files\MySQL\MySQL Shell 8.0\bin\;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\AppData\Roaming\npm;D:\nvm;C:\Program Files\nodejs;D:\work\graalvm-jdk-17_windows-x64_bin\graalvm-jdk-17.0.9+11.1\bin\;D:\work\graalvm-jdk-17_windows-x64_bin\graalvm-jdk-17.0.9+11.1\jre\bin\;C:\Users\Administrator\AppData\Local\GitHubDesktop\bin;C:\Users\Administrator\.dotnet\tools;D:\work\mongosh\;;C:\Users\Administrator\AppData\Local\Programs\Ollama"
loading library C:\Users\ADMINI1\AppData\Local\Temp\ollama1513712000\runners\cuda_v11.3\ext_server.dll
time=2024-04-01T08:23:20.407+08:00 level=INFO source=dyn_ext_server.go:87 msg="Loading Dynamic llm server: C:\Users\ADMINI~1\AppData\Local\Temp\ollama1513712000\runners\cuda_v11.3\ext_server.dll"
time=2024-04-01T08:23:20.407+08:00 level=INFO source=dyn_ext_server.go:147 msg="Initializing llama server"
llama_model_loader: loaded meta data with 24 key-value pairs and 254 tensors from D:\ollama\blobs\sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9 (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 = gemma
llama_model_loader: - kv 1: general.name str = gemma-7b-it
llama_model_loader: - kv 2: gemma.context_length u32 = 8192
llama_model_loader: - kv 3: gemma.embedding_length u32 = 3072
llama_model_loader: - kv 4: gemma.block_count u32 = 28
llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 24576
llama_model_loader: - kv 6: gemma.attention.head_count u32 = 16
llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 16
llama_model_loader: - kv 8: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 9: gemma.attention.key_length u32 = 256
llama_model_loader: - kv 10: gemma.attention.value_length u32 = 256
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,256000] = ["", "", "", "", ...
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,256000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 21: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'...
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - kv 23: general.file_type u32 = 2
llama_model_loader: - type f32: 57 tensors
llama_model_loader: - type q4_0: 196 tensors
llama_model_loader: - type q8_0: 1 tensors
llm_load_vocab: mismatch in special tokens definition ( 416/256000 vs 260/256000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = gemma
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 256000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 3072
llm_load_print_meta: n_head = 16
llm_load_print_meta: n_head_kv = 16
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_rot = 192
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 24576
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 8192
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 8.54 B
llm_load_print_meta: model size = 4.84 GiB (4.87 BPW)
llm_load_print_meta: general.name = gemma-7b-it
llm_load_print_meta: BOS token = 2 ''
llm_load_print_meta: EOS token = 1 ''
llm_load_print_meta: UNK token = 3 ''
llm_load_print_meta: PAD token = 0 ''
llm_load_print_meta: LF token = 227 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: GeForce RTX 3050 Laptop GPU, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.19 MiB
llm_load_tensors: offloading 11 repeating layers to GPU
llm_load_tensors: offloaded 11/29 layers to GPU
llm_load_tensors: CPU buffer size = 4955.54 MiB
llm_load_tensors: CUDA0 buffer size = 1633.76 MiB
...........................................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 544.00 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 352.00 MiB
llama_new_context_with_model: KV self size = 896.00 MiB, K (f16): 448.00 MiB, V (f16): 448.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 506.00 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 1302.88 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 20.00 MiB
llama_new_context_with_model: graph nodes = 957
llama_new_context_with_model: graph splits = 191
CUDA error: CUBLAS_STATUS_ALLOC_FAILED
current device: 0, in function cublas_handle at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:659
cublasCreate_v2(&cublas_handles[device])
GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error"

What did you expect to see?

No response

Steps to reproduce

No response

Are there any recent changes that introduced the issue?

No response

OS

Windows

Architecture

No response

Platform

No response

Ollama version

No response

GPU

No response

GPU info

GeForce RTX 3050 Laptop GPU

CPU

Intel

Other software

No response

Originally created by @nanshaws on GitHub (Apr 1, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/3430 ### What is the issue? time=2024-04-01T08:23:20.161+08:00 level=INFO source=gpu.go:115 msg="Detecting GPU type" time=2024-04-01T08:23:20.161+08:00 level=INFO source=gpu.go:265 msg="Searching for GPU management library cudart64_*.dll" time=2024-04-01T08:23:20.170+08:00 level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [C:\\Users\\Administrator\\AppData\\Local\\Programs\\Ollama\\cudart64_110.dll c:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\bin\\cudart64_110.dll C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\bin\\cudart64_110.dll]" time=2024-04-01T08:23:20.234+08:00 level=INFO source=gpu.go:120 msg="Nvidia GPU detected via cudart" time=2024-04-01T08:23:20.235+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-01T08:23:20.382+08:00 level=INFO source=gpu.go:188 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6" time=2024-04-01T08:23:20.382+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-01T08:23:20.382+08:00 level=INFO source=gpu.go:188 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6" time=2024-04-01T08:23:20.383+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-01T08:23:20.383+08:00 level=INFO source=assets.go:108 msg="Updating PATH to C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\ollama1513712000\\runners\\cuda_v11.3;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\bin;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\libnvvp;C:\\Program Files (x86)\\jdk/bin;D:\\work\\graalvm-jdk-17_windows-x64_bin\\graalvm-jdk-17.0.9+11.1\\bin;D:\\WindowsVSC\\VC\\Tools\\MSVC\\14.36.32532\\bin\\Hostx64\\x64\\;C:\\Program Files\\PlasticSCM5\\server;C:\\Program Files\\PlasticSCM5\\client;C:\\Windows\\system32;C:\\Windows;C:\\Windows\\System32\\Wbem;C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\;C:\\Windows\\System32\\OpenSSH\\;D:\\work\\apache-tomcat-9.0.1-windows-x64\\apache-tomcat-9.0.1\\bin\\;D:\\work\\apache-maven-3.8.8-bin\\apache-maven-3.8.8\\bin\\;D:\\work\\gradle-8.2.1-all\\gradle-8.2.1\\bin;D:\\work\\apache-jmeter-5.5\\bin;D:\\work\\w64devkit-1.19.0\\w64devkit\\bin;C:\\Program Files\\Docker\\Docker\\resources\\bin;C:\\Program Files\\MySQL\\MySQL Server 8.0\\bin;D:\\Git\\cmd;D:\\python\\;D:\\nvm;C:\\Program Files\\nodejs;D:\\work\\visualvm_216\\bin;D:\\HashiCorp\\Vagrant\\bin;D:\\weixin\\微信web开发者工具\\dll;D:\\work\\netcat-win32-1.12;D:\\work\\VMware-ovftool-4.5.0-20459872-win.x86_64\\ovftool;D:\\work\\lu;D:\\work\\kotlin-compiler-1.9.22\\kotlinc\\bin;C:\\Program Files\\CMake\\bin;C:\\Program Files\\NVIDIA Corporation\\Nsight Compute 2020.3.0\\;D:\\miniconda3;D:\\miniconda3\\Library\\mingw-w64\\bin;D:\\miniconda3\\Library\\usr\\bin;D:\\miniconda3\\Library\\bin;D:\\miniconda3\\Scripts;C:\\Program Files\\MySQL\\MySQL Shell 8.0\\bin\\;C:\\Users\\Administrator\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\Administrator\\AppData\\Roaming\\npm;D:\\nvm;C:\\Program Files\\nodejs;D:\\work\\graalvm-jdk-17_windows-x64_bin\\graalvm-jdk-17.0.9+11.1\\bin\\;D:\\work\\graalvm-jdk-17_windows-x64_bin\\graalvm-jdk-17.0.9+11.1\\jre\\bin\\;C:\\Users\\Administrator\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\Administrator\\.dotnet\\tools;D:\\work\\mongosh\\;;C:\\Users\\Administrator\\AppData\\Local\\Programs\\Ollama" loading library C:\Users\ADMINI~1\AppData\Local\Temp\ollama1513712000\runners\cuda_v11.3\ext_server.dll time=2024-04-01T08:23:20.407+08:00 level=INFO source=dyn_ext_server.go:87 msg="Loading Dynamic llm server: C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\ollama1513712000\\runners\\cuda_v11.3\\ext_server.dll" time=2024-04-01T08:23:20.407+08:00 level=INFO source=dyn_ext_server.go:147 msg="Initializing llama server" llama_model_loader: loaded meta data with 24 key-value pairs and 254 tensors from D:\ollama\blobs\sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9 (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 = gemma llama_model_loader: - kv 1: general.name str = gemma-7b-it llama_model_loader: - kv 2: gemma.context_length u32 = 8192 llama_model_loader: - kv 3: gemma.embedding_length u32 = 3072 llama_model_loader: - kv 4: gemma.block_count u32 = 28 llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 24576 llama_model_loader: - kv 6: gemma.attention.head_count u32 = 16 llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 16 llama_model_loader: - kv 8: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 9: gemma.attention.key_length u32 = 256 llama_model_loader: - kv 10: gemma.attention.value_length u32 = 256 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>", ... llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,256000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 2 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 3 llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 21: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'... llama_model_loader: - kv 22: general.quantization_version u32 = 2 llama_model_loader: - kv 23: general.file_type u32 = 2 llama_model_loader: - type f32: 57 tensors llama_model_loader: - type q4_0: 196 tensors llama_model_loader: - type q8_0: 1 tensors llm_load_vocab: mismatch in special tokens definition ( 416/256000 vs 260/256000 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = gemma llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 256000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 3072 llm_load_print_meta: n_head = 16 llm_load_print_meta: n_head_kv = 16 llm_load_print_meta: n_layer = 28 llm_load_print_meta: n_rot = 192 llm_load_print_meta: n_embd_head_k = 256 llm_load_print_meta: n_embd_head_v = 256 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 4096 llm_load_print_meta: n_embd_v_gqa = 4096 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 24576 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 8192 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 7B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 8.54 B llm_load_print_meta: model size = 4.84 GiB (4.87 BPW) llm_load_print_meta: general.name = gemma-7b-it llm_load_print_meta: BOS token = 2 '<bos>' llm_load_print_meta: EOS token = 1 '<eos>' llm_load_print_meta: UNK token = 3 '<unk>' llm_load_print_meta: PAD token = 0 '<pad>' llm_load_print_meta: LF token = 227 '<0x0A>' ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes ggml_cuda_init: found 1 CUDA devices: Device 0: GeForce RTX 3050 Laptop GPU, compute capability 8.6, VMM: yes llm_load_tensors: ggml ctx size = 0.19 MiB llm_load_tensors: offloading 11 repeating layers to GPU llm_load_tensors: offloaded 11/29 layers to GPU llm_load_tensors: CPU buffer size = 4955.54 MiB llm_load_tensors: CUDA0 buffer size = 1633.76 MiB ........................................................................... llama_new_context_with_model: n_ctx = 2048 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA_Host KV buffer size = 544.00 MiB llama_kv_cache_init: CUDA0 KV buffer size = 352.00 MiB llama_new_context_with_model: KV self size = 896.00 MiB, K (f16): 448.00 MiB, V (f16): 448.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 506.00 MiB llama_new_context_with_model: CUDA0 compute buffer size = 1302.88 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 20.00 MiB llama_new_context_with_model: graph nodes = 957 llama_new_context_with_model: graph splits = 191 CUDA error: CUBLAS_STATUS_ALLOC_FAILED current device: 0, in function cublas_handle at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:659 cublasCreate_v2(&cublas_handles[device]) GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error" ### What did you expect to see? _No response_ ### Steps to reproduce _No response_ ### Are there any recent changes that introduced the issue? _No response_ ### OS Windows ### Architecture _No response_ ### Platform _No response_ ### Ollama version _No response_ ### GPU _No response_ ### GPU info GeForce RTX 3050 Laptop GPU ### CPU Intel ### Other software _No response_
GiteaMirror added the bug label 2026-04-12 12:21:08 -05:00
GiteaMirror commented 2026-04-12 12:21:09 -05:00
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@RangerMauve commented on GitHub (May 7, 2024):

What was the fix for this? I am facing this with rocm now.

<!-- gh-comment-id:2099326818 --> @RangerMauve commented on GitHub (May 7, 2024): What was the fix for this? I am facing this with rocm now.
GiteaMirror commented 2026-04-12 12:21:10 -05:00
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@nanshaws commented on GitHub (May 14, 2024):

What was the fix for this? I am facing this with rocm now.

It may be caused by insufficient GPU memory.

<!-- gh-comment-id:2110813386 --> @nanshaws commented on GitHub (May 14, 2024): > What was the fix for this? I am facing this with rocm now. It may be caused by insufficient GPU memory.
GiteaMirror commented 2026-04-12 12:21:10 -05:00
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@RangerMauve commented on GitHub (May 15, 2024):

Oh good to know, ty. I'll try messing with my allocations.

<!-- gh-comment-id:2112706419 --> @RangerMauve commented on GitHub (May 15, 2024): Oh good to know, ty. I'll try messing with my allocations.
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