github-starred/ollama
2
0
Fork 0
mirror of https://github.com/ollama/ollama.git synced 2026-05-07 00:22:43 -05:00
Code Issues 14.5k Packages Projects Releases 120 Wiki Activity

[GH-ISSUE #3431] CUDA Error on remote connection. out of memory #2115

New Issue
Closed
opened 2026-04-12 12:21:11 -05:00 by GiteaMirror · 9 comments
GiteaMirror commented 2026-04-12 12:21:11 -05:00
Owner
Copy Link

Originally created by @systerchristian on GitHub (Apr 1, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/3431

Originally assigned to: @dhiltgen on GitHub.

What is the issue?

Getting a "CUDA Error: out of memory error" with command-r after message is returned. I am seeing this with Open Web-UI. Error is after it responds to a message. It happens every time.

Here is the tail end of the log file.

llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = command-r
llama_model_loader: - kv 1: general.name str = c4ai-command-r-v01
llama_model_loader: - kv 2: command-r.block_count u32 = 40
llama_model_loader: - kv 3: command-r.context_length u32 = 131072
llama_model_loader: - kv 4: command-r.embedding_length u32 = 8192
llama_model_loader: - kv 5: command-r.feed_forward_length u32 = 22528
llama_model_loader: - kv 6: command-r.attention.head_count u32 = 64
llama_model_loader: - kv 7: command-r.attention.head_count_kv u32 = 64
llama_model_loader: - kv 8: command-r.rope.freq_base f32 = 8000000.000000
llama_model_loader: - kv 9: command-r.attention.layer_norm_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 12
llama_model_loader: - kv 11: command-r.logit_scale f32 = 0.062500
llama_model_loader: - kv 12: command-r.rope.scaling.type str = none
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,256000] = ["", "", "", "", ...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, ...
llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,253333] = ["Ġ Ġ", "Ġ t", "e r", "i n", "Ġ a...
llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 5
llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 255001
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - type f32: 41 tensors
llama_model_loader: - type q3_K: 160 tensors
llama_model_loader: - type q4_K: 116 tensors
llama_model_loader: - type q5_K: 4 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens definition check successful ( 1008/256000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = command-r
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 256000
llm_load_print_meta: n_merges = 253333
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 64
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 8192
llm_load_print_meta: n_embd_v_gqa = 8192
llm_load_print_meta: f_norm_eps = 1.0e-05
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
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 = 6.2e-02
llm_load_print_meta: n_ff = 22528
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 = 0
llm_load_print_meta: rope scaling = none
llm_load_print_meta: freq_base_train = 8000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 131072
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 = 35B
llm_load_print_meta: model ftype = Q3_K - Medium
llm_load_print_meta: model params = 34.98 B
llm_load_print_meta: model size = 16.40 GiB (4.03 BPW)
llm_load_print_meta: general.name = c4ai-command-r-v01
llm_load_print_meta: BOS token = 5 '<BOS_TOKEN>'
llm_load_print_meta: EOS token = 255001 '<|END_OF_TURN_TOKEN|>'
llm_load_print_meta: PAD token = 0 ''
llm_load_print_meta: LF token = 136 'Ä'
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: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size = 0.25 MiB
llm_load_tensors: offloading 40 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 41/41 layers to GPU
llm_load_tensors: CPU buffer size = 1640.62 MiB
llm_load_tensors: CUDA0 buffer size = 16791.91 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 = 8000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 2560.00 MiB
llama_new_context_with_model: KV self size = 2560.00 MiB, K (f16): 1280.00 MiB, V (f16): 1280.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 516.00 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 516.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 20.00 MiB
llama_new_context_with_model: graph nodes = 1245
llama_new_context_with_model: graph splits = 2
{"function":"initialize","level":"INFO","line":444,"msg":"initializing slots","n_slots":1,"tid":"240","timestamp":1711936563}
{"function":"initialize","level":"INFO","line":456,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"240","timestamp":1711936563}
time=2024-03-31T18:56:03.805-07:00 level=INFO source=dyn_ext_server.go:159 msg="Starting llama main loop"
{"function":"update_slots","level":"INFO","line":1572,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"35172","timestamp":1711936563}
[GIN] 2024/03/31 - 18:56:03 | 200 | 9.3664512s | 192.168.50.60 | POST "/api/chat"
{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936570}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":8,"slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936570}
{"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936570}
{"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 353.31 ms / 8 tokens ( 44.16 ms per token, 22.64 tokens per second)","n_prompt_tokens_processed":8,"n_tokens_second":22.64332517230155,"slot_id":0,"t_prompt_processing":353.305,"t_token":44.163125,"task_id":0,"tid":"35172","timestamp":1711936571}
{"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 739.95 ms / 29 runs ( 25.52 ms per token, 39.19 tokens per second)","n_decoded":29,"n_tokens_second":39.191678390384254,"slot_id":0,"t_token":25.515620689655172,"t_token_generation":739.953,"task_id":0,"tid":"35172","timestamp":1711936571}
{"function":"print_timings","level":"INFO","line":295,"msg":" total time = 1093.26 ms","slot_id":0,"t_prompt_processing":353.305,"t_token_generation":739.953,"t_total":1093.258,"task_id":0,"tid":"35172","timestamp":1711936571}
{"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":37,"n_ctx":2048,"n_past":36,"n_system_tokens":0,"slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936571,"truncated":false}
[GIN] 2024/03/31 - 18:56:11 | 200 | 1.0944379s | 192.168.50.60 | POST "/api/chat"
{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936587}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":36,"n_past_se":0,"n_prompt_tokens_processed":10,"slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936587}
{"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":36,"slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936587}
{"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 289.25 ms / 10 tokens ( 28.92 ms per token, 34.57 tokens per second)","n_prompt_tokens_processed":10,"n_tokens_second":34.57228892753302,"slot_id":0,"t_prompt_processing":289.249,"t_token":28.9249,"task_id":32,"tid":"35172","timestamp":1711936589}
{"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 1058.89 ms / 41 runs ( 25.83 ms per token, 38.72 tokens per second)","n_decoded":41,"n_tokens_second":38.719828046188034,"slot_id":0,"t_token":25.826560975609752,"t_token_generation":1058.889,"task_id":32,"tid":"35172","timestamp":1711936589}
{"function":"print_timings","level":"INFO","line":295,"msg":" total time = 1348.14 ms","slot_id":0,"t_prompt_processing":289.249,"t_token_generation":1058.889,"t_total":1348.138,"task_id":32,"tid":"35172","timestamp":1711936589}
{"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":86,"n_ctx":2048,"n_past":86,"n_system_tokens":0,"slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936589,"truncated":false}
[GIN] 2024/03/31 - 18:56:29 | 200 | 1.3504819s | 192.168.50.60 | POST "/api/chat"
{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936611}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":84,"n_past_se":0,"n_prompt_tokens_processed":9,"slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936611}
{"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":84,"slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936611}
{"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 322.37 ms / 9 tokens ( 35.82 ms per token, 27.92 tokens per second)","n_prompt_tokens_processed":9,"n_tokens_second":27.918230604584796,"slot_id":0,"t_prompt_processing":322.37,"t_token":35.81888888888889,"task_id":76,"tid":"35172","timestamp":1711936612}
{"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 1279.00 ms / 49 runs ( 26.10 ms per token, 38.31 tokens per second)","n_decoded":49,"n_tokens_second":38.31106079418048,"slot_id":0,"t_token":26.10212244897959,"t_token_generation":1279.004,"task_id":76,"tid":"35172","timestamp":1711936612}
{"function":"print_timings","level":"INFO","line":295,"msg":" total time = 1601.37 ms","slot_id":0,"t_prompt_processing":322.37,"t_token_generation":1279.004,"t_total":1601.3739999999998,"task_id":76,"tid":"35172","timestamp":1711936612}
{"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":138,"n_ctx":2048,"n_past":141,"n_system_tokens":0,"slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936612,"truncated":false}
[GIN] 2024/03/31 - 18:56:52 | 200 | 1.6048077s | 192.168.50.60 | POST "/api/chat"
{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":128,"tid":"35172","timestamp":1711936639}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":3,"n_past_se":0,"n_prompt_tokens_processed":48,"slot_id":0,"task_id":128,"tid":"35172","timestamp":1711936639}
{"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":3,"slot_id":0,"task_id":128,"tid":"35172","timestamp":1711936639}
CUDA error: out of memory
current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:532
cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1)
GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error"

What did you expect to see?

Ollama to not crash. :)

Steps to reproduce

Any message sent while a Command-r model is loaded. Have been able to replicate with both lastest and command-r:35b-v0.1-q3_K_M. Both models appear to work fine from the console.

Are there any recent changes that introduced the issue?

Nope. Tho I haven't been working with Ollama long.

OS

Windows

Architecture

x86

Platform

No response

Ollama version

0.1.30

GPU

Nvidia

GPU info

Sun Mar 31 19:13:31 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 551.86 Driver Version: 551.86 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4090 WDDM | 00000000:01:00.0 Off | Off |
| 0% 48C P8 18W / 450W | 2107MiB / 24564MiB | 8% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 6840 C+G ...ft Office\root\Office16\ONENOTE.EXE N/A |
| 0 N/A N/A 7160 C+G ...ekyb3d8bbwe\PhoneExperienceHost.exe N/A |
| 0 N/A N/A 8716 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
| 0 N/A N/A 10984 C+G ...sair iCUE5 Software\QmlRenderer.exe N/A |
| 0 N/A N/A 14044 C+G ...wekyb3d8bbwe\XboxGameBarWidgets.exe N/A |
| 0 N/A N/A 15560 C+G ...\cef\cef.win7x64\steamwebhelper.exe N/A |
| 0 N/A N/A 16252 C+G ...on\123.0.2420.65\msedgewebview2.exe N/A |
| 0 N/A N/A 17256 C+G ...\LM-Studio\app-0.2.18\LM Studio.exe N/A |
| 0 N/A N/A 20184 C+G ...les\Microsoft OneDrive\OneDrive.exe N/A |
| 0 N/A N/A 21284 C+G ...siveControlPanel\SystemSettings.exe N/A |
| 0 N/A N/A 21684 C+G ...crosoft\Edge\Application\msedge.exe N/A |
| 0 N/A N/A 22008 C+G ...oogle\Chrome\Application\chrome.exe N/A |
| 0 N/A N/A 23756 C+G ...air\Corsair iCUE5 Software\iCUE.exe N/A |
| 0 N/A N/A 24504 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 N/A N/A 25468 C+G ...e Stream\88.0.0.0\GoogleDriveFS.exe N/A |
| 0 N/A N/A 25996 C+G ...__8wekyb3d8bbwe\WindowsTerminal.exe N/A |
| 0 N/A N/A 28544 C ...\LM-Studio\app-0.2.18\LM Studio.exe N/A |
| 0 N/A N/A 29564 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 30092 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 31484 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A |
| 0 N/A N/A 35296 C+G ...__8wekyb3d8bbwe\Notepad\Notepad.exe N/A |
| 0 N/A N/A 36216 C+G ...on\123.0.2420.65\msedgewebview2.exe N/A |
+-----------------------------------------------------------------------------------------+

CPU

Intel

Other software

Open Web-ui

Originally created by @systerchristian on GitHub (Apr 1, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/3431 Originally assigned to: @dhiltgen on GitHub. ### What is the issue? Getting a "CUDA Error: out of memory error" with command-r after message is returned. I am seeing this with Open Web-UI. Error is after it responds to a message. It happens every time. Here is the tail end of the log file. llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = command-r llama_model_loader: - kv 1: general.name str = c4ai-command-r-v01 llama_model_loader: - kv 2: command-r.block_count u32 = 40 llama_model_loader: - kv 3: command-r.context_length u32 = 131072 llama_model_loader: - kv 4: command-r.embedding_length u32 = 8192 llama_model_loader: - kv 5: command-r.feed_forward_length u32 = 22528 llama_model_loader: - kv 6: command-r.attention.head_count u32 = 64 llama_model_loader: - kv 7: command-r.attention.head_count_kv u32 = 64 llama_model_loader: - kv 8: command-r.rope.freq_base f32 = 8000000.000000 llama_model_loader: - kv 9: command-r.attention.layer_norm_epsilon f32 = 0.000010 llama_model_loader: - kv 10: general.file_type u32 = 12 llama_model_loader: - kv 11: command-r.logit_scale f32 = 0.062500 llama_model_loader: - kv 12: command-r.rope.scaling.type str = none llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 14: tokenizer.ggml.tokens arr[str,256000] = ["<PAD>", "<UNK>", "<CLS>", "<SEP>", ... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, ... llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,253333] = ["Ġ Ġ", "Ġ t", "e r", "i n", "Ġ a... llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 5 llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 255001 llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 22: general.quantization_version u32 = 2 llama_model_loader: - type f32: 41 tensors llama_model_loader: - type q3_K: 160 tensors llama_model_loader: - type q4_K: 116 tensors llama_model_loader: - type q5_K: 4 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens definition check successful ( 1008/256000 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = command-r llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 256000 llm_load_print_meta: n_merges = 253333 llm_load_print_meta: n_ctx_train = 131072 llm_load_print_meta: n_embd = 8192 llm_load_print_meta: n_head = 64 llm_load_print_meta: n_head_kv = 64 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 8192 llm_load_print_meta: n_embd_v_gqa = 8192 llm_load_print_meta: f_norm_eps = 1.0e-05 llm_load_print_meta: f_norm_rms_eps = 0.0e+00 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 = 6.2e-02 llm_load_print_meta: n_ff = 22528 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 = 0 llm_load_print_meta: rope scaling = none llm_load_print_meta: freq_base_train = 8000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 131072 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 = 35B llm_load_print_meta: model ftype = Q3_K - Medium llm_load_print_meta: model params = 34.98 B llm_load_print_meta: model size = 16.40 GiB (4.03 BPW) llm_load_print_meta: general.name = c4ai-command-r-v01 llm_load_print_meta: BOS token = 5 '<BOS_TOKEN>' llm_load_print_meta: EOS token = 255001 '<|END_OF_TURN_TOKEN|>' llm_load_print_meta: PAD token = 0 '<PAD>' llm_load_print_meta: LF token = 136 'Ä' 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: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes llm_load_tensors: ggml ctx size = 0.25 MiB llm_load_tensors: offloading 40 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 41/41 layers to GPU llm_load_tensors: CPU buffer size = 1640.62 MiB llm_load_tensors: CUDA0 buffer size = 16791.91 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 = 8000000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 2560.00 MiB llama_new_context_with_model: KV self size = 2560.00 MiB, K (f16): 1280.00 MiB, V (f16): 1280.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 516.00 MiB llama_new_context_with_model: CUDA0 compute buffer size = 516.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 20.00 MiB llama_new_context_with_model: graph nodes = 1245 llama_new_context_with_model: graph splits = 2 {"function":"initialize","level":"INFO","line":444,"msg":"initializing slots","n_slots":1,"tid":"240","timestamp":1711936563} {"function":"initialize","level":"INFO","line":456,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"240","timestamp":1711936563} time=2024-03-31T18:56:03.805-07:00 level=INFO source=dyn_ext_server.go:159 msg="Starting llama main loop" {"function":"update_slots","level":"INFO","line":1572,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"35172","timestamp":1711936563} [GIN] 2024/03/31 - 18:56:03 | 200 | 9.3664512s | 192.168.50.60 | POST "/api/chat" {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936570} {"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":8,"slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936570} {"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936570} {"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 353.31 ms / 8 tokens ( 44.16 ms per token, 22.64 tokens per second)","n_prompt_tokens_processed":8,"n_tokens_second":22.64332517230155,"slot_id":0,"t_prompt_processing":353.305,"t_token":44.163125,"task_id":0,"tid":"35172","timestamp":1711936571} {"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 739.95 ms / 29 runs ( 25.52 ms per token, 39.19 tokens per second)","n_decoded":29,"n_tokens_second":39.191678390384254,"slot_id":0,"t_token":25.515620689655172,"t_token_generation":739.953,"task_id":0,"tid":"35172","timestamp":1711936571} {"function":"print_timings","level":"INFO","line":295,"msg":" total time = 1093.26 ms","slot_id":0,"t_prompt_processing":353.305,"t_token_generation":739.953,"t_total":1093.258,"task_id":0,"tid":"35172","timestamp":1711936571} {"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":37,"n_ctx":2048,"n_past":36,"n_system_tokens":0,"slot_id":0,"task_id":0,"tid":"35172","timestamp":1711936571,"truncated":false} [GIN] 2024/03/31 - 18:56:11 | 200 | 1.0944379s | 192.168.50.60 | POST "/api/chat" {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936587} {"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":36,"n_past_se":0,"n_prompt_tokens_processed":10,"slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936587} {"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":36,"slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936587} {"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 289.25 ms / 10 tokens ( 28.92 ms per token, 34.57 tokens per second)","n_prompt_tokens_processed":10,"n_tokens_second":34.57228892753302,"slot_id":0,"t_prompt_processing":289.249,"t_token":28.9249,"task_id":32,"tid":"35172","timestamp":1711936589} {"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 1058.89 ms / 41 runs ( 25.83 ms per token, 38.72 tokens per second)","n_decoded":41,"n_tokens_second":38.719828046188034,"slot_id":0,"t_token":25.826560975609752,"t_token_generation":1058.889,"task_id":32,"tid":"35172","timestamp":1711936589} {"function":"print_timings","level":"INFO","line":295,"msg":" total time = 1348.14 ms","slot_id":0,"t_prompt_processing":289.249,"t_token_generation":1058.889,"t_total":1348.138,"task_id":32,"tid":"35172","timestamp":1711936589} {"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":86,"n_ctx":2048,"n_past":86,"n_system_tokens":0,"slot_id":0,"task_id":32,"tid":"35172","timestamp":1711936589,"truncated":false} [GIN] 2024/03/31 - 18:56:29 | 200 | 1.3504819s | 192.168.50.60 | POST "/api/chat" {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936611} {"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":84,"n_past_se":0,"n_prompt_tokens_processed":9,"slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936611} {"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":84,"slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936611} {"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 322.37 ms / 9 tokens ( 35.82 ms per token, 27.92 tokens per second)","n_prompt_tokens_processed":9,"n_tokens_second":27.918230604584796,"slot_id":0,"t_prompt_processing":322.37,"t_token":35.81888888888889,"task_id":76,"tid":"35172","timestamp":1711936612} {"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 1279.00 ms / 49 runs ( 26.10 ms per token, 38.31 tokens per second)","n_decoded":49,"n_tokens_second":38.31106079418048,"slot_id":0,"t_token":26.10212244897959,"t_token_generation":1279.004,"task_id":76,"tid":"35172","timestamp":1711936612} {"function":"print_timings","level":"INFO","line":295,"msg":" total time = 1601.37 ms","slot_id":0,"t_prompt_processing":322.37,"t_token_generation":1279.004,"t_total":1601.3739999999998,"task_id":76,"tid":"35172","timestamp":1711936612} {"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":138,"n_ctx":2048,"n_past":141,"n_system_tokens":0,"slot_id":0,"task_id":76,"tid":"35172","timestamp":1711936612,"truncated":false} [GIN] 2024/03/31 - 18:56:52 | 200 | 1.6048077s | 192.168.50.60 | POST "/api/chat" {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":128,"tid":"35172","timestamp":1711936639} {"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":3,"n_past_se":0,"n_prompt_tokens_processed":48,"slot_id":0,"task_id":128,"tid":"35172","timestamp":1711936639} {"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":3,"slot_id":0,"task_id":128,"tid":"35172","timestamp":1711936639} CUDA error: out of memory current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:532 cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1) GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error" ### What did you expect to see? Ollama to not crash. :) ### Steps to reproduce Any message sent while a Command-r model is loaded. Have been able to replicate with both lastest and command-r:35b-v0.1-q3_K_M. Both models appear to work fine from the console. ### Are there any recent changes that introduced the issue? Nope. Tho I haven't been working with Ollama long. ### OS Windows ### Architecture x86 ### Platform _No response_ ### Ollama version 0.1.30 ### GPU Nvidia ### GPU info Sun Mar 31 19:13:31 2024 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 551.86 Driver Version: 551.86 CUDA Version: 12.4 | |-----------------------------------------+------------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 4090 WDDM | 00000000:01:00.0 Off | Off | | 0% 48C P8 18W / 450W | 2107MiB / 24564MiB | 8% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 6840 C+G ...ft Office\root\Office16\ONENOTE.EXE N/A | | 0 N/A N/A 7160 C+G ...ekyb3d8bbwe\PhoneExperienceHost.exe N/A | | 0 N/A N/A 8716 C+G ...2txyewy\StartMenuExperienceHost.exe N/A | | 0 N/A N/A 10984 C+G ...sair iCUE5 Software\QmlRenderer.exe N/A | | 0 N/A N/A 14044 C+G ...wekyb3d8bbwe\XboxGameBarWidgets.exe N/A | | 0 N/A N/A 15560 C+G ...\cef\cef.win7x64\steamwebhelper.exe N/A | | 0 N/A N/A 16252 C+G ...on\123.0.2420.65\msedgewebview2.exe N/A | | 0 N/A N/A 17256 C+G ...\LM-Studio\app-0.2.18\LM Studio.exe N/A | | 0 N/A N/A 20184 C+G ...les\Microsoft OneDrive\OneDrive.exe N/A | | 0 N/A N/A 21284 C+G ...siveControlPanel\SystemSettings.exe N/A | | 0 N/A N/A 21684 C+G ...crosoft\Edge\Application\msedge.exe N/A | | 0 N/A N/A 22008 C+G ...oogle\Chrome\Application\chrome.exe N/A | | 0 N/A N/A 23756 C+G ...air\Corsair iCUE5 Software\iCUE.exe N/A | | 0 N/A N/A 24504 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A | | 0 N/A N/A 25468 C+G ...e Stream\88.0.0.0\GoogleDriveFS.exe N/A | | 0 N/A N/A 25996 C+G ...__8wekyb3d8bbwe\WindowsTerminal.exe N/A | | 0 N/A N/A 28544 C ...\LM-Studio\app-0.2.18\LM Studio.exe N/A | | 0 N/A N/A 29564 C+G C:\Windows\explorer.exe N/A | | 0 N/A N/A 30092 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A | | 0 N/A N/A 31484 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A | | 0 N/A N/A 35296 C+G ...__8wekyb3d8bbwe\Notepad\Notepad.exe N/A | | 0 N/A N/A 36216 C+G ...on\123.0.2420.65\msedgewebview2.exe N/A | +-----------------------------------------------------------------------------------------+ ### CPU Intel ### Other software Open Web-ui
GiteaMirror added the bugnvidia labels 2026-04-12 12:21:11 -05:00
GiteaMirror commented 2026-04-12 12:21:12 -05:00
Author
Owner
Copy Link

@aluhrs13 commented on GitHub (Apr 4, 2024):

+1, also hitting this I think. Can get detailed logs or anything if needed.

<!-- gh-comment-id:2036051167 --> @aluhrs13 commented on GitHub (Apr 4, 2024): +1, also hitting this I think. Can get detailed logs or anything if needed.
GiteaMirror commented 2026-04-12 12:21:13 -05:00
Author
Owner
Copy Link

@FonzieBonzo commented on GitHub (Apr 4, 2024):

Same here using NVidia 2070 (8GB) on WIndows 11.
Client using Ollama with openwebui from another (linux) pc

First query works, second i get (and ollama stops) :
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: CUDA0 KV buffer size = 896.00 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 = 506.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 10.00 MiB
llama_new_context_with_model: graph nodes = 957
llama_new_context_with_model: graph splits = 2
{"function":"initialize","level":"INFO","line":444,"msg":"initializing slots","n_slots":1,"tid":"10524","timestamp":1711990697}
{"function":"initialize","level":"INFO","line":456,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"10524","timestamp":1711990697}
time=2024-04-01T18:58:17.918+02:00 level=INFO source=dyn_ext_server.go:159 msg="Starting llama main loop"
{"function":"update_slots","level":"INFO","line":1572,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"3400","timestamp":1711990697}
{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990697}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":16,"slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990697}
{"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990697}
{"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 93.09 ms / 16 tokens ( 5.82 ms per token, 171.87 tokens per second)","n_prompt_tokens_processed":16,"n_tokens_second":171.87298586344693,"slot_id":0,"t_prompt_processing":93.092,"t_token":5.81825,"task_id":0,"tid":"3400","timestamp":1711990702}
{"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 4741.50 ms / 254 runs ( 18.67 ms per token, 53.57 tokens per second)","n_decoded":254,"n_tokens_second":53.569590694582466,"slot_id":0,"t_token":18.667307086614173,"t_token_generation":4741.496,"task_id":0,"tid":"3400","timestamp":1711990702}
{"function":"print_timings","level":"INFO","line":295,"msg":" total time = 4834.59 ms","slot_id":0,"t_prompt_processing":93.092,"t_token_generation":4741.496,"t_total":4834.588,"task_id":0,"tid":"3400","timestamp":1711990702}
{"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":270,"n_ctx":2048,"n_past":269,"n_system_tokens":0,"slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990702,"truncated":false}
[GIN] 2024/04/01 - 18:58:22 | 200 | 9.0355119s | 10.0.0.4 | POST "/api/chat"
{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":257,"tid":"3400","timestamp":1711990702}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":4,"n_past_se":0,"n_prompt_tokens_processed":50,"slot_id":0,"task_id":257,"tid":"3400","timestamp":1711990702}
{"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":4,"slot_id":0,"task_id":257,"tid":"3400","timestamp":1711990702}
CUDA error: out of memory
current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:532
cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1)
GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error"

I have no experience with the language "GO" but if u need something to test...

<!-- gh-comment-id:2036312433 --> @FonzieBonzo commented on GitHub (Apr 4, 2024): Same here using NVidia 2070 (8GB) on WIndows 11. Client using Ollama with openwebui from another (linux) pc First query works, second i get (and ollama stops) : 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: CUDA0 KV buffer size = 896.00 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 = 506.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 10.00 MiB llama_new_context_with_model: graph nodes = 957 llama_new_context_with_model: graph splits = 2 {"function":"initialize","level":"INFO","line":444,"msg":"initializing slots","n_slots":1,"tid":"10524","timestamp":1711990697} {"function":"initialize","level":"INFO","line":456,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"10524","timestamp":1711990697} time=2024-04-01T18:58:17.918+02:00 level=INFO source=dyn_ext_server.go:159 msg="Starting llama main loop" {"function":"update_slots","level":"INFO","line":1572,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"3400","timestamp":1711990697} {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990697} {"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":16,"slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990697} {"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990697} {"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 93.09 ms / 16 tokens ( 5.82 ms per token, 171.87 tokens per second)","n_prompt_tokens_processed":16,"n_tokens_second":171.87298586344693,"slot_id":0,"t_prompt_processing":93.092,"t_token":5.81825,"task_id":0,"tid":"3400","timestamp":1711990702} {"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 4741.50 ms / 254 runs ( 18.67 ms per token, 53.57 tokens per second)","n_decoded":254,"n_tokens_second":53.569590694582466,"slot_id":0,"t_token":18.667307086614173,"t_token_generation":4741.496,"task_id":0,"tid":"3400","timestamp":1711990702} {"function":"print_timings","level":"INFO","line":295,"msg":" total time = 4834.59 ms","slot_id":0,"t_prompt_processing":93.092,"t_token_generation":4741.496,"t_total":4834.588,"task_id":0,"tid":"3400","timestamp":1711990702} {"function":"update_slots","level":"INFO","line":1642,"msg":"slot released","n_cache_tokens":270,"n_ctx":2048,"n_past":269,"n_system_tokens":0,"slot_id":0,"task_id":0,"tid":"3400","timestamp":1711990702,"truncated":false} [GIN] 2024/04/01 - 18:58:22 | 200 | 9.0355119s | 10.0.0.4 | POST "/api/chat" {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":257,"tid":"3400","timestamp":1711990702} {"function":"update_slots","ga_i":0,"level":"INFO","line":1810,"msg":"slot progression","n_past":4,"n_past_se":0,"n_prompt_tokens_processed":50,"slot_id":0,"task_id":257,"tid":"3400","timestamp":1711990702} {"function":"update_slots","level":"INFO","line":1834,"msg":"kv cache rm [p0, end)","p0":4,"slot_id":0,"task_id":257,"tid":"3400","timestamp":1711990702} CUDA error: out of memory current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:532 cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1) GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error" I have no experience with the language "GO" but if u need something to test...
GiteaMirror commented 2026-04-12 12:21:13 -05:00
Author
Owner
Copy Link

@systerchristian commented on GitHub (Apr 4, 2024):

In my testing, I did see that forcing the following in the modelfile improved stability:

PARAMETER num_ctx 4196
PARAMETER num_gpu ((Any number greater than 41 worked)

<!-- gh-comment-id:2037765550 --> @systerchristian commented on GitHub (Apr 4, 2024): In my testing, I did see that forcing the following in the modelfile improved stability: PARAMETER num_ctx 4196 PARAMETER num_gpu ((Any number greater than 41 worked)
GiteaMirror commented 2026-04-12 12:21:13 -05:00
Author
Owner
Copy Link

@zigak1 commented on GitHub (Apr 4, 2024):

Have the same issue on Ubuntu 22.04, RTX 2080 Ti, nvidia drivers: 535.161.07 , CUDA version 12.2

Tried with multiple different ollama versions, nvidia drivers, cuda versions, cuda toolkit version. Nothing helps.

time=2024-04-04T13:17:16.349+02:00 level=INFO source=images.go:806 msg="total blobs: 5"
time=2024-04-04T13:17:16.349+02:00 level=INFO source=images.go:813 msg="total unused blobs removed: 0"
time=2024-04-04T13:17:16.349+02:00 level=INFO source=routes.go:1110 msg="Listening on 127.0.0.1:11434 (version 0.1.29)"
time=2024-04-04T13:17:16.349+02:00 level=INFO source=payload_common.go:112 msg="Extracting dynamic libraries to /tmp/ollama3032254341/runners ..."
time=2024-04-04T13:17:18.918+02:00 level=INFO source=payload_common.go:139 msg="Dynamic LLM libraries [rocm_v60000 cpu cpu_avx2 cuda_v11 cpu_avx]"
time=2024-04-04T13:17:18.918+02:00 level=INFO source=gpu.go:77 msg="Detecting GPU type"
time=2024-04-04T13:17:18.918+02:00 level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-04T13:17:18.920+02:00 level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.535.161.07]"
time=2024-04-04T13:17:18.927+02:00 level=INFO source=gpu.go:82 msg="Nvidia GPU detected"
time=2024-04-04T13:17:18.927+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-04T13:17:18.932+02:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 7.5"
[GIN] 2024/04/04 - 13:18:00 | 200 | 39.782µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/04/04 - 13:18:00 | 200 | 917.981µs | 127.0.0.1 | POST "/api/show"
[GIN] 2024/04/04 - 13:18:00 | 200 | 265.456µs | 127.0.0.1 | POST "/api/show"
time=2024-04-04T13:18:01.786+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-04T13:18:01.787+02:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 7.5"
time=2024-04-04T13:18:01.787+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-04T13:18:01.787+02:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 7.5"
time=2024-04-04T13:18:01.787+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
loading library /tmp/ollama3032254341/runners/cuda_v11/libext_server.so
time=2024-04-04T13:18:01.793+02:00 level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: /tmp/ollama3032254341/runners/cuda_v11/libext_server.so"
time=2024-04-04T13:18:01.793+02:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server"
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: yes
ggml_init_cublas: CUDA_USE_TENSOR_CORES: no
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 2080 Ti, compute capability 7.5, VMM: yes
llama_model_loader: loaded meta data with 21 key-value pairs and 164 tensors from /home/ml/.ollama/models/blobs/sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 (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-2b-it
llama_model_loader: - kv 2: gemma.context_length u32 = 8192
llama_model_loader: - kv 3: gemma.block_count u32 = 18
llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048
llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8
llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1
llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256
llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256
llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["", "", "", "", ...
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: general.quantization_version u32 = 2
llama_model_loader: - kv 20: general.file_type u32 = 2
llama_model_loader: - type f32: 37 tensors
llama_model_loader: - type q4_0: 126 tensors
llama_model_loader: - type q8_0: 1 tensors
llm_load_vocab: mismatch in special tokens definition ( 544/256128 vs 388/256128 ).
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 = 256128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 8
llm_load_print_meta: n_head_kv = 1
llm_load_print_meta: n_layer = 18
llm_load_print_meta: n_rot = 256
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
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: n_ff = 16384
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
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: model type = 2B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 2.51 B
llm_load_print_meta: model size = 1.56 GiB (5.34 BPW)
llm_load_print_meta: general.name = gemma-2b-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>'
llm_load_tensors: ggml ctx size = 0.13 MiB
llm_load_tensors: offloading 18 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 19/19 layers to GPU
llm_load_tensors: CPU buffer size = 531.52 MiB
llm_load_tensors: CUDA0 buffer size = 1594.93 MiB
.....................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 36.00 MiB
llama_new_context_with_model: KV self size = 36.00 MiB, K (f16): 18.00 MiB, V (f16): 18.00 MiB
llama_new_context_with_model: CUDA_Host input buffer size = 9.02 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 504.25 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 4.00 MiB
llama_new_context_with_model: graph splits (measure): 2
CUDA error: an illegal memory access was encountered
current device: 0, in function ggml_backend_cuda_get_tensor_async at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:12223
cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0])
GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:256: !"CUDA error"
Could not attach to process. If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Operation not permitted.
No stack.
The program is not being run.
SIGABRT: abort
PC=0x73ed1c8969fc m=7 sigcode=18446744073709551610
signal arrived during cgo execution

goroutine 50 gp=0xc0000aae00 m=7 mp=0xc000580008 [syscall]:
runtime.cgocall(0xebb2b0, 0xc00004c6f8)
runtime/cgocall.go:157 +0x4b fp=0xc00004c6d0 sp=0xc00004c698 pc=0x40a72b
github.com/jmorganca/ollama/llm._Cfunc_dyn_llama_server_init({0x73ecbc013ed0, 0x73ec96090e90, 0x73ec96080a20, 0x73ec960902e0, 0x73ec96095cb0, 0x73ec9608a940, 0x73ec96083650, 0x73ec96080aa0, 0x73ec960965b0, 0x73ec96095850, ...}, ...)
_cgo_gotypes.go:286 +0x45 fp=0xc00004c6f8 sp=0xc00004c6d0 pc=0xce5ca5
github.com/jmorganca/ollama/llm.newDynExtServer.func7(0xc00007e500, 0xc001a6f050)
github.com/jmorganca/ollama/llm/dyn_ext_server.go:154 +0x112 fp=0xc00004c838 sp=0xc00004c6f8 pc=0xce7352
github.com/jmorganca/ollama/llm.newDynExtServer({0xc0005c8000, 0x37}, {0xc00067eaf0, }, {, _, }, {0x0, 0x0, 0x0}, ...)
github.com/jmorganca/ollama/llm/dyn_ext_server.go:154 +0xb50 fp=0xc00004ca80 sp=0xc00004c838 pc=0xce6f90
github.com/jmorganca/ollama/llm.newLlmServer({{, _, }, {, }, {, }}, {, _}, {0x0, ...}, ...)
github.com/jmorganca/ollama/llm/llm.go:166 +0x4c5 fp=0xc00004cc40 sp=0xc00004ca80 pc=0xce3525
github.com/jmorganca/ollama/llm.New({0xc00067eaf0, 0x65}, {0x0, 0x0, 0x0}, {0x0, _, _}, {{0x0, 0x800, ...}, ...})
github.com/jmorganca/ollama/llm/llm.go:131 +0x90e fp=0xc00004ced8 sp=0xc00004cc40 pc=0xce2ece
github.com/jmorganca/ollama/server.load(0xc000003500?, 0xc000003500, {{0x0, 0x800, 0x200, 0x1, 0xffffffffffffffff, 0x0, 0x0, 0x1, ...}, ...}, ...)
github.com/jmorganca/ollama/server/routes.go:86 +0x325 fp=0xc00004d028 sp=0xc00004ced8 pc=0xe93c65
github.com/jmorganca/ollama/server.ChatHandler(0xc0000e8c00)
github.com/jmorganca/ollama/server/routes.go:1264 +0xa4b fp=0xc00004d730 sp=0xc00004d028 pc=0xe9f96b
github.com/gin-gonic/gin.(*Context).Next(0xc0000e8c00)
github.com/gin-gonic/gin@v1.9.1/context.go:174 +0x2b fp=0xc00004d750 sp=0xc00004d730 pc=0xe674eb
github.com/jmorganca/ollama/server.(*Server).GenerateRoutes.allowedHostsMiddleware.func3(0xc0000e8c00)
github.com/jmorganca/ollama/server/routes.go:1001 +0x115 fp=0xc00004d7a8 sp=0xc00004d750 pc=0xe9e0d5
github.com/gin-gonic/gin.(*Context).Next(...)
github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.CustomRecoveryWithWriter.func1(0xc0000e8c00)
github.com/gin-gonic/gin@v1.9.1/recovery.go:102 +0x7a fp=0xc00004d7f8 sp=0xc00004d7a8 pc=0xe743da
github.com/gin-gonic/gin.(*Context).Next(...)
github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.LoggerWithConfig.func1(0xc0000e8c00)
github.com/gin-gonic/gin@v1.9.1/logger.go:240 +0xdd fp=0xc00004d9a8 sp=0xc00004d7f8 pc=0xe7351d
github.com/gin-gonic/gin.(*Context).Next(...)
github.com/gin-gonic/gin@v1.9.1/context.go:174
github.com/gin-gonic/gin.(*Engine).handleHTTPRequest(0xc0005101a0, 0xc0000e8c00)
github.com/gin-gonic/gin@v1.9.1/gin.go:620 +0x66e fp=0xc00004db28 sp=0xc00004d9a8 pc=0xe72a0e
github.com/gin-gonic/gin.(*Engine).ServeHTTP(0xc0005101a0, {0x11654090, 0xc0006161c0}, 0xc0005230e0)
github.com/gin-gonic/gin@v1.9.1/gin.go:576 +0x1b2 fp=0xc00004db60 sp=0xc00004db28 pc=0xe721d2
net/http.serverHandler.ServeHTTP({0x11651f70?}, {0x11654090?, 0xc0006161c0?}, 0x6?)
net/http/server.go:3137 +0x8e fp=0xc00004db90 sp=0xc00004db60 pc=0x6fef4e
net/http.(*conn).serve(0xc00017a1b0, {0x11656448, 0xc0000c2f30})
net/http/server.go:2039 +0x5e8 fp=0xc00004dfb8 sp=0xc00004db90 pc=0x6fa308
net/http.(*Server).Serve.gowrap3()
net/http/server.go:3285 +0x28 fp=0xc00004dfe0 sp=0xc00004dfb8 pc=0x6ff768
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc00004dfe8 sp=0xc00004dfe0 pc=0x4742e1
created by net/http.(*Server).Serve in goroutine 1
net/http/server.go:3285 +0x4b4

goroutine 1 gp=0xc0000061c0 m=nil [IO wait]:
runtime.gopark(0xc00005d408?, 0x0?, 0xc0?, 0x61?, 0xc0008c3868?)
runtime/proc.go:402 +0xce fp=0xc0008c3830 sp=0xc0008c3810 pc=0x44160e
runtime.netpollblock(0xc0008c38c8?, 0x409ec6?, 0x0?)
runtime/netpoll.go:573 +0xf7 fp=0xc0008c3868 sp=0xc0008c3830 pc=0x43a377
internal/poll.runtime_pollWait(0x73ed1ca566d0, 0x72)
runtime/netpoll.go:345 +0x85 fp=0xc0008c3888 sp=0xc0008c3868 pc=0x46e9e5
internal/poll.(*pollDesc).wait(0x3?, 0x3fe?, 0x0)
internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0008c38b0 sp=0xc0008c3888 pc=0x5030a7
internal/poll.(*pollDesc).waitRead(...)
internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Accept(0xc0000e4400)
internal/poll/fd_unix.go:611 +0x2ac fp=0xc0008c3958 sp=0xc0008c38b0 pc=0x50844c
net.(*netFD).accept(0xc0000e4400)
net/fd_unix.go:172 +0x29 fp=0xc0008c3a10 sp=0xc0008c3958 pc=0x597c49
net.(*TCPListener).accept(0xc000514820)
net/tcpsock_posix.go:159 +0x1e fp=0xc0008c3a38 sp=0xc0008c3a10 pc=0x5adb7e
net.(*TCPListener).Accept(0xc000514820)
net/tcpsock.go:327 +0x30 fp=0xc0008c3a68 sp=0xc0008c3a38 pc=0x5acd70
net/http.(*onceCloseListener).Accept(0xc00017a1b0?)
:1 +0x24 fp=0xc0008c3a80 sp=0xc0008c3a68 pc=0x7219a4
net/http.(*Server).Serve(0xc0004ae000, {0x11653e20, 0xc000514820})
net/http/server.go:3255 +0x33e fp=0xc0008c3bb0 sp=0xc0008c3a80 pc=0x6ff37e
github.com/jmorganca/ollama/server.Serve({0x11653e20, 0xc000514820})
github.com/jmorganca/ollama/server/routes.go:1137 +0x4bf fp=0xc0008c3cc0 sp=0xc0008c3bb0 pc=0xe9e61f
github.com/jmorganca/ollama/cmd.RunServer(0xc0000e8a00?, {0x11da2340?, 0x4?, 0x1050bd5?})
github.com/jmorganca/ollama/cmd/cmd.go:787 +0x1b9 fp=0xc0008c3d58 sp=0xc0008c3cc0 pc=0xeb2219
github.com/spf13/cobra.(*Command).execute(0xc0000d0f08, {0x11da2340, 0x0, 0x0})
github.com/spf13/cobra@v1.7.0/command.go:940 +0x882 fp=0xc0008c3e78 sp=0xc0008c3d58 pc=0x794922
github.com/spf13/cobra.(*Command).ExecuteC(0xc0000d0308)
github.com/spf13/cobra@v1.7.0/command.go:1068 +0x3a5 fp=0xc0008c3f30 sp=0xc0008c3e78 pc=0x795165
github.com/spf13/cobra.(*Command).Execute(...)
github.com/spf13/cobra@v1.7.0/command.go:992
github.com/spf13/cobra.(*Command).ExecuteContext(...)
github.com/spf13/cobra@v1.7.0/command.go:985
main.main()
github.com/jmorganca/ollama/main.go:11 +0x4d fp=0xc0008c3f50 sp=0xc0008c3f30 pc=0xeba3cd
runtime.main()
runtime/proc.go:271 +0x29d fp=0xc0008c3fe0 sp=0xc0008c3f50 pc=0x4411dd
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0008c3fe8 sp=0xc0008c3fe0 pc=0x4742e1

goroutine 2 gp=0xc000006c40 m=nil [force gc (idle)]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000074fa8 sp=0xc000074f88 pc=0x44160e
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.forcegchelper()
runtime/proc.go:326 +0xb3 fp=0xc000074fe0 sp=0xc000074fa8 pc=0x441493
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000074fe8 sp=0xc000074fe0 pc=0x4742e1
created by runtime.init.6 in goroutine 1
runtime/proc.go:314 +0x1a

goroutine 3 gp=0xc000007180 m=nil [GC sweep wait]:
runtime.gopark(0x1?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000075780 sp=0xc000075760 pc=0x44160e
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.bgsweep(0xc000046070)
runtime/mgcsweep.go:318 +0xdf fp=0xc0000757c8 sp=0xc000075780 pc=0x42cbbf
runtime.gcenable.gowrap1()
runtime/mgc.go:203 +0x25 fp=0xc0000757e0 sp=0xc0000757c8 pc=0x4214a5
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000757e8 sp=0xc0000757e0 pc=0x4742e1
created by runtime.gcenable in goroutine 1
runtime/mgc.go:203 +0x66

goroutine 4 gp=0xc000007340 m=nil [GC scavenge wait]:
runtime.gopark(0x88b129d?, 0x881f5b3?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000075f78 sp=0xc000075f58 pc=0x44160e
runtime.goparkunlock(...)
runtime/proc.go:408
runtime.(*scavengerState).park(0x11d3c300)
runtime/mgcscavenge.go:425 +0x49 fp=0xc000075fa8 sp=0xc000075f78 pc=0x42a549
runtime.bgscavenge(0xc000046070)
runtime/mgcscavenge.go:658 +0x59 fp=0xc000075fc8 sp=0xc000075fa8 pc=0x42aaf9
runtime.gcenable.gowrap2()
runtime/mgc.go:204 +0x25 fp=0xc000075fe0 sp=0xc000075fc8 pc=0x421445
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000075fe8 sp=0xc000075fe0 pc=0x4742e1
created by runtime.gcenable in goroutine 1
runtime/mgc.go:204 +0xa5

goroutine 5 gp=0xc000007c00 m=nil [finalizer wait]:
runtime.gopark(0xc000074648?, 0x414865?, 0xa8?, 0x1?, 0xc0000061c0?)
runtime/proc.go:402 +0xce fp=0xc000074620 sp=0xc000074600 pc=0x44160e
runtime.runfinq()
runtime/mfinal.go:194 +0x107 fp=0xc0000747e0 sp=0xc000074620 pc=0x4204e7
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000747e8 sp=0xc0000747e0 pc=0x4742e1
created by runtime.createfing in goroutine 1
runtime/mfinal.go:164 +0x3d

goroutine 6 gp=0xc00015d6c0 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c96dd?, 0x1?, 0x9c?, 0x6c?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000076750 sp=0xc000076730 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc0000767e0 sp=0xc000076750 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000767e8 sp=0xc0000767e0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 7 gp=0xc00015d880 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c968b?, 0x1?, 0xdd?, 0x34?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000076f50 sp=0xc000076f30 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000076fe0 sp=0xc000076f50 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000076fe8 sp=0xc000076fe0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 8 gp=0xc00015da40 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c9760?, 0x3?, 0xde?, 0x4d?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000077750 sp=0xc000077730 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc0000777e0 sp=0xc000077750 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000777e8 sp=0xc0000777e0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 9 gp=0xc00015dc00 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c9745?, 0x3?, 0xe1?, 0xc1?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000077f50 sp=0xc000077f30 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000077fe0 sp=0xc000077f50 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000077fe8 sp=0xc000077fe0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 18 gp=0xc000500000 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c97ef?, 0x3?, 0x44?, 0x27?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000070750 sp=0xc000070730 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc0000707e0 sp=0xc000070750 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000707e8 sp=0xc0000707e0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 19 gp=0xc0005001c0 m=nil [GC worker (idle)]:
runtime.gopark(0x4d5665686167?, 0x1?, 0x1f?, 0x1b?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000070f50 sp=0xc000070f30 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc000070fe0 sp=0xc000070f50 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc000070fe8 sp=0xc000070fe0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 20 gp=0xc000500380 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c96b7?, 0x3?, 0x8?, 0x96?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc000071750 sp=0xc000071730 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc0000717e0 sp=0xc000071750 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000717e8 sp=0xc0000717e0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 34 gp=0xc0000aa380 m=nil [GC worker (idle)]:
runtime.gopark(0x4d56728c9ddb?, 0x3?, 0x71?, 0x15?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc0000bc750 sp=0xc0000bc730 pc=0x44160e
runtime.gcBgMarkWorker()
runtime/mgc.go:1310 +0xe5 fp=0xc0000bc7e0 sp=0xc0000bc750 pc=0x423585
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000bc7e8 sp=0xc0000bc7e0 pc=0x4742e1
created by runtime.gcBgMarkStartWorkers in goroutine 1
runtime/mgc.go:1234 +0x1c

goroutine 10 gp=0xc000500540 m=nil [select, locked to thread]:
runtime.gopark(0xc0000befa8?, 0x2?, 0xa9?, 0x18?, 0xc0000bef94?)
runtime/proc.go:402 +0xce fp=0xc0000bee38 sp=0xc0000bee18 pc=0x44160e
runtime.selectgo(0xc0000befa8, 0xc0000bef90, 0x0?, 0x0, 0x0?, 0x1)
runtime/select.go:327 +0x725 fp=0xc0000bef58 sp=0xc0000bee38 pc=0x452a65
runtime.ensureSigM.func1()
runtime/signal_unix.go:1034 +0x19f fp=0xc0000befe0 sp=0xc0000bef58 pc=0x46b71f
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000befe8 sp=0xc0000befe0 pc=0x4742e1
created by runtime.ensureSigM in goroutine 1
runtime/signal_unix.go:1017 +0xc8

goroutine 11 gp=0xc000500700 m=9 mp=0xc000680008 [syscall]:
runtime.notetsleepg(0x11da2fc0, 0xffffffffffffffff)
runtime/lock_futex.go:246 +0x29 fp=0xc0000bf7a0 sp=0xc0000bf778 pc=0x412e89
os/signal.signal_recv()
runtime/sigqueue.go:152 +0x29 fp=0xc0000bf7c0 sp=0xc0000bf7a0 pc=0x470d49
os/signal.loop()
os/signal/signal_unix.go:23 +0x13 fp=0xc0000bf7e0 sp=0xc0000bf7c0 pc=0x723d53
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000bf7e8 sp=0xc0000bf7e0 pc=0x4742e1
created by os/signal.Notify.func1.1 in goroutine 1
os/signal/signal.go:151 +0x1f

goroutine 21 gp=0xc0000aa540 m=nil [chan receive]:
runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?)
runtime/proc.go:402 +0xce fp=0xc0000ba720 sp=0xc0000ba700 pc=0x44160e
runtime.chanrecv(0xc0000d6d20, 0x0, 0x1)
runtime/chan.go:583 +0x3bf fp=0xc0000ba798 sp=0xc0000ba720 pc=0x40cd3f
runtime.chanrecv1(0x0?, 0x0?)
runtime/chan.go:442 +0x12 fp=0xc0000ba7c0 sp=0xc0000ba798 pc=0x40c952
github.com/jmorganca/ollama/server.Serve.func2()
github.com/jmorganca/ollama/server/routes.go:1119 +0x19 fp=0xc0000ba7e0 sp=0xc0000ba7c0 pc=0xe9e699
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000ba7e8 sp=0xc0000ba7e0 pc=0x4742e1
created by github.com/jmorganca/ollama/server.Serve in goroutine 1
github.com/jmorganca/ollama/server/routes.go:1118 +0x40e

goroutine 36 gp=0xc0000aafc0 m=nil [IO wait]:
runtime.gopark(0x10?, 0x10?, 0xf0?, 0xd5?, 0xb?)
runtime/proc.go:402 +0xce fp=0xc0000bd5a8 sp=0xc0000bd588 pc=0x44160e
runtime.netpollblock(0x4863f8?, 0x409ec6?, 0x0?)
runtime/netpoll.go:573 +0xf7 fp=0xc0000bd5e0 sp=0xc0000bd5a8 pc=0x43a377
internal/poll.runtime_pollWait(0x73ed1ca565d8, 0x72)
runtime/netpoll.go:345 +0x85 fp=0xc0000bd600 sp=0xc0000bd5e0 pc=0x46e9e5
internal/poll.(*pollDesc).wait(0xc00017e680?, 0xc0000c3031?, 0x0)
internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0000bd628 sp=0xc0000bd600 pc=0x5030a7
internal/poll.(*pollDesc).waitRead(...)
internal/poll/fd_poll_runtime.go:89
internal/poll.(*FD).Read(0xc00017e680, {0xc0000c3031, 0x1, 0x1})
internal/poll/fd_unix.go:164 +0x27a fp=0xc0000bd6c0 sp=0xc0000bd628 pc=0x50439a
net.(*netFD).Read(0xc00017e680, {0xc0000c3031?, 0xc0000bd748?, 0x470a50?})
net/fd_posix.go:55 +0x25 fp=0xc0000bd708 sp=0xc0000bd6c0 pc=0x595c65
net.(*conn).Read(0xc0000c6130, {0xc0000c3031?, 0x0?, 0x11da2340?})
net/net.go:179 +0x45 fp=0xc0000bd750 sp=0xc0000bd708 pc=0x5a4ac5
net.(*TCPConn).Read(0x11caba40?, {0xc0000c3031?, 0xc0000c84c0?, 0x7ebe40?})
:1 +0x25 fp=0xc0000bd780 sp=0xc0000bd750 pc=0x5b6145
net/http.(*connReader).backgroundRead(0xc0000c3020)
net/http/server.go:681 +0x37 fp=0xc0000bd7c8 sp=0xc0000bd780 pc=0x6f4277
net/http.(*connReader).startBackgroundRead.gowrap2()
net/http/server.go:677 +0x25 fp=0xc0000bd7e0 sp=0xc0000bd7c8 pc=0x6f41a5
runtime.goexit({})
runtime/asm_amd64.s:1695 +0x1 fp=0xc0000bd7e8 sp=0xc0000bd7e0 pc=0x4742e1
created by net/http.(*connReader).startBackgroundRead in goroutine 50
net/http/server.go:677 +0xba

rax 0x0
rbx 0x73ecc6ffd640
rcx 0x73ed1c8969fc
rdx 0x6
rdi 0x348c
rsi 0x3492
rbp 0x3492
rsp 0x73ecc6ffc150
r8 0x73ecc6ffc220
r9 0x73ecc6ffc1c0
r10 0x8
r11 0x246
r12 0x6
r13 0x16
r14 0x73ecc4475f90
r15 0x73ec97f97440
rip 0x73ed1c8969fc
rflags 0x246
cs 0x33
fs 0x0
gs 0x0

<!-- gh-comment-id:2038181698 --> @zigak1 commented on GitHub (Apr 4, 2024): Have the same issue on Ubuntu 22.04, RTX 2080 Ti, nvidia drivers: 535.161.07 , CUDA version 12.2 Tried with multiple different ollama versions, nvidia drivers, cuda versions, cuda toolkit version. Nothing helps. time=2024-04-04T13:17:16.349+02:00 level=INFO source=images.go:806 msg="total blobs: 5" time=2024-04-04T13:17:16.349+02:00 level=INFO source=images.go:813 msg="total unused blobs removed: 0" time=2024-04-04T13:17:16.349+02:00 level=INFO source=routes.go:1110 msg="Listening on 127.0.0.1:11434 (version 0.1.29)" time=2024-04-04T13:17:16.349+02:00 level=INFO source=payload_common.go:112 msg="Extracting dynamic libraries to /tmp/ollama3032254341/runners ..." time=2024-04-04T13:17:18.918+02:00 level=INFO source=payload_common.go:139 msg="Dynamic LLM libraries [rocm_v60000 cpu cpu_avx2 cuda_v11 cpu_avx]" time=2024-04-04T13:17:18.918+02:00 level=INFO source=gpu.go:77 msg="Detecting GPU type" time=2024-04-04T13:17:18.918+02:00 level=INFO source=gpu.go:191 msg="Searching for GPU management library libnvidia-ml.so" time=2024-04-04T13:17:18.920+02:00 level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.535.161.07]" time=2024-04-04T13:17:18.927+02:00 level=INFO source=gpu.go:82 msg="Nvidia GPU detected" time=2024-04-04T13:17:18.927+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-04T13:17:18.932+02:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 7.5" [GIN] 2024/04/04 - 13:18:00 | 200 | 39.782µs | 127.0.0.1 | HEAD "/" [GIN] 2024/04/04 - 13:18:00 | 200 | 917.981µs | 127.0.0.1 | POST "/api/show" [GIN] 2024/04/04 - 13:18:00 | 200 | 265.456µs | 127.0.0.1 | POST "/api/show" time=2024-04-04T13:18:01.786+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-04T13:18:01.787+02:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 7.5" time=2024-04-04T13:18:01.787+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-04T13:18:01.787+02:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 7.5" time=2024-04-04T13:18:01.787+02:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" loading library /tmp/ollama3032254341/runners/cuda_v11/libext_server.so time=2024-04-04T13:18:01.793+02:00 level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: /tmp/ollama3032254341/runners/cuda_v11/libext_server.so" time=2024-04-04T13:18:01.793+02:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server" ggml_init_cublas: GGML_CUDA_FORCE_MMQ: yes ggml_init_cublas: CUDA_USE_TENSOR_CORES: no ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 2080 Ti, compute capability 7.5, VMM: yes llama_model_loader: loaded meta data with 21 key-value pairs and 164 tensors from /home/ml/.ollama/models/blobs/sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 (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-2b-it llama_model_loader: - kv 2: gemma.context_length u32 = 8192 llama_model_loader: - kv 3: gemma.block_count u32 = 18 llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048 llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384 llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8 llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1 llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256 llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256 llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2 llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1 llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3 llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["<pad>", "<eos>", "<bos>", "<unk>", ... llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 19: general.quantization_version u32 = 2 llama_model_loader: - kv 20: general.file_type u32 = 2 llama_model_loader: - type f32: 37 tensors llama_model_loader: - type q4_0: 126 tensors llama_model_loader: - type q8_0: 1 tensors llm_load_vocab: mismatch in special tokens definition ( 544/256128 vs 388/256128 ). 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 = 256128 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 2048 llm_load_print_meta: n_head = 8 llm_load_print_meta: n_head_kv = 1 llm_load_print_meta: n_layer = 18 llm_load_print_meta: n_rot = 256 llm_load_print_meta: n_embd_head_k = 256 llm_load_print_meta: n_embd_head_v = 256 llm_load_print_meta: n_gqa = 8 llm_load_print_meta: n_embd_k_gqa = 256 llm_load_print_meta: n_embd_v_gqa = 256 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: n_ff = 16384 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 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: model type = 2B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 2.51 B llm_load_print_meta: model size = 1.56 GiB (5.34 BPW) llm_load_print_meta: general.name = gemma-2b-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>' llm_load_tensors: ggml ctx size = 0.13 MiB llm_load_tensors: offloading 18 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 19/19 layers to GPU llm_load_tensors: CPU buffer size = 531.52 MiB llm_load_tensors: CUDA0 buffer size = 1594.93 MiB ..................................................... llama_new_context_with_model: n_ctx = 2048 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 36.00 MiB llama_new_context_with_model: KV self size = 36.00 MiB, K (f16): 18.00 MiB, V (f16): 18.00 MiB llama_new_context_with_model: CUDA_Host input buffer size = 9.02 MiB llama_new_context_with_model: CUDA0 compute buffer size = 504.25 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 4.00 MiB llama_new_context_with_model: graph splits (measure): 2 CUDA error: an illegal memory access was encountered current device: 0, in function ggml_backend_cuda_get_tensor_async at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:12223 cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]) GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/ggml-cuda.cu:256: !"CUDA error" Could not attach to process. If your uid matches the uid of the target process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf ptrace: Operation not permitted. No stack. The program is not being run. SIGABRT: abort PC=0x73ed1c8969fc m=7 sigcode=18446744073709551610 signal arrived during cgo execution goroutine 50 gp=0xc0000aae00 m=7 mp=0xc000580008 [syscall]: runtime.cgocall(0xebb2b0, 0xc00004c6f8) runtime/cgocall.go:157 +0x4b fp=0xc00004c6d0 sp=0xc00004c698 pc=0x40a72b github.com/jmorganca/ollama/llm._Cfunc_dyn_llama_server_init({0x73ecbc013ed0, 0x73ec96090e90, 0x73ec96080a20, 0x73ec960902e0, 0x73ec96095cb0, 0x73ec9608a940, 0x73ec96083650, 0x73ec96080aa0, 0x73ec960965b0, 0x73ec96095850, ...}, ...) _cgo_gotypes.go:286 +0x45 fp=0xc00004c6f8 sp=0xc00004c6d0 pc=0xce5ca5 github.com/jmorganca/ollama/llm.newDynExtServer.func7(0xc00007e500, 0xc001a6f050) github.com/jmorganca/ollama/llm/dyn_ext_server.go:154 +0x112 fp=0xc00004c838 sp=0xc00004c6f8 pc=0xce7352 github.com/jmorganca/ollama/llm.newDynExtServer({0xc0005c8000, 0x37}, {0xc00067eaf0, _}, {_, _, _}, {0x0, 0x0, 0x0}, ...) github.com/jmorganca/ollama/llm/dyn_ext_server.go:154 +0xb50 fp=0xc00004ca80 sp=0xc00004c838 pc=0xce6f90 github.com/jmorganca/ollama/llm.newLlmServer({{_, _, _}, {_, _}, {_, _}}, {_, _}, {0x0, ...}, ...) github.com/jmorganca/ollama/llm/llm.go:166 +0x4c5 fp=0xc00004cc40 sp=0xc00004ca80 pc=0xce3525 github.com/jmorganca/ollama/llm.New({0xc00067eaf0, 0x65}, {0x0, 0x0, 0x0}, {0x0, _, _}, {{0x0, 0x800, ...}, ...}) github.com/jmorganca/ollama/llm/llm.go:131 +0x90e fp=0xc00004ced8 sp=0xc00004cc40 pc=0xce2ece github.com/jmorganca/ollama/server.load(0xc000003500?, 0xc000003500, {{0x0, 0x800, 0x200, 0x1, 0xffffffffffffffff, 0x0, 0x0, 0x1, ...}, ...}, ...) github.com/jmorganca/ollama/server/routes.go:86 +0x325 fp=0xc00004d028 sp=0xc00004ced8 pc=0xe93c65 github.com/jmorganca/ollama/server.ChatHandler(0xc0000e8c00) github.com/jmorganca/ollama/server/routes.go:1264 +0xa4b fp=0xc00004d730 sp=0xc00004d028 pc=0xe9f96b github.com/gin-gonic/gin.(*Context).Next(0xc0000e8c00) github.com/gin-gonic/gin@v1.9.1/context.go:174 +0x2b fp=0xc00004d750 sp=0xc00004d730 pc=0xe674eb github.com/jmorganca/ollama/server.(*Server).GenerateRoutes.allowedHostsMiddleware.func3(0xc0000e8c00) github.com/jmorganca/ollama/server/routes.go:1001 +0x115 fp=0xc00004d7a8 sp=0xc00004d750 pc=0xe9e0d5 github.com/gin-gonic/gin.(*Context).Next(...) github.com/gin-gonic/gin@v1.9.1/context.go:174 github.com/gin-gonic/gin.CustomRecoveryWithWriter.func1(0xc0000e8c00) github.com/gin-gonic/gin@v1.9.1/recovery.go:102 +0x7a fp=0xc00004d7f8 sp=0xc00004d7a8 pc=0xe743da github.com/gin-gonic/gin.(*Context).Next(...) github.com/gin-gonic/gin@v1.9.1/context.go:174 github.com/gin-gonic/gin.LoggerWithConfig.func1(0xc0000e8c00) github.com/gin-gonic/gin@v1.9.1/logger.go:240 +0xdd fp=0xc00004d9a8 sp=0xc00004d7f8 pc=0xe7351d github.com/gin-gonic/gin.(*Context).Next(...) github.com/gin-gonic/gin@v1.9.1/context.go:174 github.com/gin-gonic/gin.(*Engine).handleHTTPRequest(0xc0005101a0, 0xc0000e8c00) github.com/gin-gonic/gin@v1.9.1/gin.go:620 +0x66e fp=0xc00004db28 sp=0xc00004d9a8 pc=0xe72a0e github.com/gin-gonic/gin.(*Engine).ServeHTTP(0xc0005101a0, {0x11654090, 0xc0006161c0}, 0xc0005230e0) github.com/gin-gonic/gin@v1.9.1/gin.go:576 +0x1b2 fp=0xc00004db60 sp=0xc00004db28 pc=0xe721d2 net/http.serverHandler.ServeHTTP({0x11651f70?}, {0x11654090?, 0xc0006161c0?}, 0x6?) net/http/server.go:3137 +0x8e fp=0xc00004db90 sp=0xc00004db60 pc=0x6fef4e net/http.(*conn).serve(0xc00017a1b0, {0x11656448, 0xc0000c2f30}) net/http/server.go:2039 +0x5e8 fp=0xc00004dfb8 sp=0xc00004db90 pc=0x6fa308 net/http.(*Server).Serve.gowrap3() net/http/server.go:3285 +0x28 fp=0xc00004dfe0 sp=0xc00004dfb8 pc=0x6ff768 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc00004dfe8 sp=0xc00004dfe0 pc=0x4742e1 created by net/http.(*Server).Serve in goroutine 1 net/http/server.go:3285 +0x4b4 goroutine 1 gp=0xc0000061c0 m=nil [IO wait]: runtime.gopark(0xc00005d408?, 0x0?, 0xc0?, 0x61?, 0xc0008c3868?) runtime/proc.go:402 +0xce fp=0xc0008c3830 sp=0xc0008c3810 pc=0x44160e runtime.netpollblock(0xc0008c38c8?, 0x409ec6?, 0x0?) runtime/netpoll.go:573 +0xf7 fp=0xc0008c3868 sp=0xc0008c3830 pc=0x43a377 internal/poll.runtime_pollWait(0x73ed1ca566d0, 0x72) runtime/netpoll.go:345 +0x85 fp=0xc0008c3888 sp=0xc0008c3868 pc=0x46e9e5 internal/poll.(*pollDesc).wait(0x3?, 0x3fe?, 0x0) internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0008c38b0 sp=0xc0008c3888 pc=0x5030a7 internal/poll.(*pollDesc).waitRead(...) internal/poll/fd_poll_runtime.go:89 internal/poll.(*FD).Accept(0xc0000e4400) internal/poll/fd_unix.go:611 +0x2ac fp=0xc0008c3958 sp=0xc0008c38b0 pc=0x50844c net.(*netFD).accept(0xc0000e4400) net/fd_unix.go:172 +0x29 fp=0xc0008c3a10 sp=0xc0008c3958 pc=0x597c49 net.(*TCPListener).accept(0xc000514820) net/tcpsock_posix.go:159 +0x1e fp=0xc0008c3a38 sp=0xc0008c3a10 pc=0x5adb7e net.(*TCPListener).Accept(0xc000514820) net/tcpsock.go:327 +0x30 fp=0xc0008c3a68 sp=0xc0008c3a38 pc=0x5acd70 net/http.(*onceCloseListener).Accept(0xc00017a1b0?) <autogenerated>:1 +0x24 fp=0xc0008c3a80 sp=0xc0008c3a68 pc=0x7219a4 net/http.(*Server).Serve(0xc0004ae000, {0x11653e20, 0xc000514820}) net/http/server.go:3255 +0x33e fp=0xc0008c3bb0 sp=0xc0008c3a80 pc=0x6ff37e github.com/jmorganca/ollama/server.Serve({0x11653e20, 0xc000514820}) github.com/jmorganca/ollama/server/routes.go:1137 +0x4bf fp=0xc0008c3cc0 sp=0xc0008c3bb0 pc=0xe9e61f github.com/jmorganca/ollama/cmd.RunServer(0xc0000e8a00?, {0x11da2340?, 0x4?, 0x1050bd5?}) github.com/jmorganca/ollama/cmd/cmd.go:787 +0x1b9 fp=0xc0008c3d58 sp=0xc0008c3cc0 pc=0xeb2219 github.com/spf13/cobra.(*Command).execute(0xc0000d0f08, {0x11da2340, 0x0, 0x0}) github.com/spf13/cobra@v1.7.0/command.go:940 +0x882 fp=0xc0008c3e78 sp=0xc0008c3d58 pc=0x794922 github.com/spf13/cobra.(*Command).ExecuteC(0xc0000d0308) github.com/spf13/cobra@v1.7.0/command.go:1068 +0x3a5 fp=0xc0008c3f30 sp=0xc0008c3e78 pc=0x795165 github.com/spf13/cobra.(*Command).Execute(...) github.com/spf13/cobra@v1.7.0/command.go:992 github.com/spf13/cobra.(*Command).ExecuteContext(...) github.com/spf13/cobra@v1.7.0/command.go:985 main.main() github.com/jmorganca/ollama/main.go:11 +0x4d fp=0xc0008c3f50 sp=0xc0008c3f30 pc=0xeba3cd runtime.main() runtime/proc.go:271 +0x29d fp=0xc0008c3fe0 sp=0xc0008c3f50 pc=0x4411dd runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0008c3fe8 sp=0xc0008c3fe0 pc=0x4742e1 goroutine 2 gp=0xc000006c40 m=nil [force gc (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000074fa8 sp=0xc000074f88 pc=0x44160e runtime.goparkunlock(...) runtime/proc.go:408 runtime.forcegchelper() runtime/proc.go:326 +0xb3 fp=0xc000074fe0 sp=0xc000074fa8 pc=0x441493 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000074fe8 sp=0xc000074fe0 pc=0x4742e1 created by runtime.init.6 in goroutine 1 runtime/proc.go:314 +0x1a goroutine 3 gp=0xc000007180 m=nil [GC sweep wait]: runtime.gopark(0x1?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000075780 sp=0xc000075760 pc=0x44160e runtime.goparkunlock(...) runtime/proc.go:408 runtime.bgsweep(0xc000046070) runtime/mgcsweep.go:318 +0xdf fp=0xc0000757c8 sp=0xc000075780 pc=0x42cbbf runtime.gcenable.gowrap1() runtime/mgc.go:203 +0x25 fp=0xc0000757e0 sp=0xc0000757c8 pc=0x4214a5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000757e8 sp=0xc0000757e0 pc=0x4742e1 created by runtime.gcenable in goroutine 1 runtime/mgc.go:203 +0x66 goroutine 4 gp=0xc000007340 m=nil [GC scavenge wait]: runtime.gopark(0x88b129d?, 0x881f5b3?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000075f78 sp=0xc000075f58 pc=0x44160e runtime.goparkunlock(...) runtime/proc.go:408 runtime.(*scavengerState).park(0x11d3c300) runtime/mgcscavenge.go:425 +0x49 fp=0xc000075fa8 sp=0xc000075f78 pc=0x42a549 runtime.bgscavenge(0xc000046070) runtime/mgcscavenge.go:658 +0x59 fp=0xc000075fc8 sp=0xc000075fa8 pc=0x42aaf9 runtime.gcenable.gowrap2() runtime/mgc.go:204 +0x25 fp=0xc000075fe0 sp=0xc000075fc8 pc=0x421445 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000075fe8 sp=0xc000075fe0 pc=0x4742e1 created by runtime.gcenable in goroutine 1 runtime/mgc.go:204 +0xa5 goroutine 5 gp=0xc000007c00 m=nil [finalizer wait]: runtime.gopark(0xc000074648?, 0x414865?, 0xa8?, 0x1?, 0xc0000061c0?) runtime/proc.go:402 +0xce fp=0xc000074620 sp=0xc000074600 pc=0x44160e runtime.runfinq() runtime/mfinal.go:194 +0x107 fp=0xc0000747e0 sp=0xc000074620 pc=0x4204e7 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000747e8 sp=0xc0000747e0 pc=0x4742e1 created by runtime.createfing in goroutine 1 runtime/mfinal.go:164 +0x3d goroutine 6 gp=0xc00015d6c0 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c96dd?, 0x1?, 0x9c?, 0x6c?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000076750 sp=0xc000076730 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0000767e0 sp=0xc000076750 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000767e8 sp=0xc0000767e0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 7 gp=0xc00015d880 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c968b?, 0x1?, 0xdd?, 0x34?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000076f50 sp=0xc000076f30 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000076fe0 sp=0xc000076f50 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000076fe8 sp=0xc000076fe0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 8 gp=0xc00015da40 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c9760?, 0x3?, 0xde?, 0x4d?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000077750 sp=0xc000077730 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0000777e0 sp=0xc000077750 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000777e8 sp=0xc0000777e0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 9 gp=0xc00015dc00 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c9745?, 0x3?, 0xe1?, 0xc1?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000077f50 sp=0xc000077f30 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000077fe0 sp=0xc000077f50 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000077fe8 sp=0xc000077fe0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 18 gp=0xc000500000 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c97ef?, 0x3?, 0x44?, 0x27?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000070750 sp=0xc000070730 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0000707e0 sp=0xc000070750 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000707e8 sp=0xc0000707e0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 19 gp=0xc0005001c0 m=nil [GC worker (idle)]: runtime.gopark(0x4d5665686167?, 0x1?, 0x1f?, 0x1b?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000070f50 sp=0xc000070f30 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc000070fe0 sp=0xc000070f50 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc000070fe8 sp=0xc000070fe0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 20 gp=0xc000500380 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c96b7?, 0x3?, 0x8?, 0x96?, 0x0?) runtime/proc.go:402 +0xce fp=0xc000071750 sp=0xc000071730 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0000717e0 sp=0xc000071750 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000717e8 sp=0xc0000717e0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 34 gp=0xc0000aa380 m=nil [GC worker (idle)]: runtime.gopark(0x4d56728c9ddb?, 0x3?, 0x71?, 0x15?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0000bc750 sp=0xc0000bc730 pc=0x44160e runtime.gcBgMarkWorker() runtime/mgc.go:1310 +0xe5 fp=0xc0000bc7e0 sp=0xc0000bc750 pc=0x423585 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000bc7e8 sp=0xc0000bc7e0 pc=0x4742e1 created by runtime.gcBgMarkStartWorkers in goroutine 1 runtime/mgc.go:1234 +0x1c goroutine 10 gp=0xc000500540 m=nil [select, locked to thread]: runtime.gopark(0xc0000befa8?, 0x2?, 0xa9?, 0x18?, 0xc0000bef94?) runtime/proc.go:402 +0xce fp=0xc0000bee38 sp=0xc0000bee18 pc=0x44160e runtime.selectgo(0xc0000befa8, 0xc0000bef90, 0x0?, 0x0, 0x0?, 0x1) runtime/select.go:327 +0x725 fp=0xc0000bef58 sp=0xc0000bee38 pc=0x452a65 runtime.ensureSigM.func1() runtime/signal_unix.go:1034 +0x19f fp=0xc0000befe0 sp=0xc0000bef58 pc=0x46b71f runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000befe8 sp=0xc0000befe0 pc=0x4742e1 created by runtime.ensureSigM in goroutine 1 runtime/signal_unix.go:1017 +0xc8 goroutine 11 gp=0xc000500700 m=9 mp=0xc000680008 [syscall]: runtime.notetsleepg(0x11da2fc0, 0xffffffffffffffff) runtime/lock_futex.go:246 +0x29 fp=0xc0000bf7a0 sp=0xc0000bf778 pc=0x412e89 os/signal.signal_recv() runtime/sigqueue.go:152 +0x29 fp=0xc0000bf7c0 sp=0xc0000bf7a0 pc=0x470d49 os/signal.loop() os/signal/signal_unix.go:23 +0x13 fp=0xc0000bf7e0 sp=0xc0000bf7c0 pc=0x723d53 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000bf7e8 sp=0xc0000bf7e0 pc=0x4742e1 created by os/signal.Notify.func1.1 in goroutine 1 os/signal/signal.go:151 +0x1f goroutine 21 gp=0xc0000aa540 m=nil [chan receive]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) runtime/proc.go:402 +0xce fp=0xc0000ba720 sp=0xc0000ba700 pc=0x44160e runtime.chanrecv(0xc0000d6d20, 0x0, 0x1) runtime/chan.go:583 +0x3bf fp=0xc0000ba798 sp=0xc0000ba720 pc=0x40cd3f runtime.chanrecv1(0x0?, 0x0?) runtime/chan.go:442 +0x12 fp=0xc0000ba7c0 sp=0xc0000ba798 pc=0x40c952 github.com/jmorganca/ollama/server.Serve.func2() github.com/jmorganca/ollama/server/routes.go:1119 +0x19 fp=0xc0000ba7e0 sp=0xc0000ba7c0 pc=0xe9e699 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000ba7e8 sp=0xc0000ba7e0 pc=0x4742e1 created by github.com/jmorganca/ollama/server.Serve in goroutine 1 github.com/jmorganca/ollama/server/routes.go:1118 +0x40e goroutine 36 gp=0xc0000aafc0 m=nil [IO wait]: runtime.gopark(0x10?, 0x10?, 0xf0?, 0xd5?, 0xb?) runtime/proc.go:402 +0xce fp=0xc0000bd5a8 sp=0xc0000bd588 pc=0x44160e runtime.netpollblock(0x4863f8?, 0x409ec6?, 0x0?) runtime/netpoll.go:573 +0xf7 fp=0xc0000bd5e0 sp=0xc0000bd5a8 pc=0x43a377 internal/poll.runtime_pollWait(0x73ed1ca565d8, 0x72) runtime/netpoll.go:345 +0x85 fp=0xc0000bd600 sp=0xc0000bd5e0 pc=0x46e9e5 internal/poll.(*pollDesc).wait(0xc00017e680?, 0xc0000c3031?, 0x0) internal/poll/fd_poll_runtime.go:84 +0x27 fp=0xc0000bd628 sp=0xc0000bd600 pc=0x5030a7 internal/poll.(*pollDesc).waitRead(...) internal/poll/fd_poll_runtime.go:89 internal/poll.(*FD).Read(0xc00017e680, {0xc0000c3031, 0x1, 0x1}) internal/poll/fd_unix.go:164 +0x27a fp=0xc0000bd6c0 sp=0xc0000bd628 pc=0x50439a net.(*netFD).Read(0xc00017e680, {0xc0000c3031?, 0xc0000bd748?, 0x470a50?}) net/fd_posix.go:55 +0x25 fp=0xc0000bd708 sp=0xc0000bd6c0 pc=0x595c65 net.(*conn).Read(0xc0000c6130, {0xc0000c3031?, 0x0?, 0x11da2340?}) net/net.go:179 +0x45 fp=0xc0000bd750 sp=0xc0000bd708 pc=0x5a4ac5 net.(*TCPConn).Read(0x11caba40?, {0xc0000c3031?, 0xc0000c84c0?, 0x7ebe40?}) <autogenerated>:1 +0x25 fp=0xc0000bd780 sp=0xc0000bd750 pc=0x5b6145 net/http.(*connReader).backgroundRead(0xc0000c3020) net/http/server.go:681 +0x37 fp=0xc0000bd7c8 sp=0xc0000bd780 pc=0x6f4277 net/http.(*connReader).startBackgroundRead.gowrap2() net/http/server.go:677 +0x25 fp=0xc0000bd7e0 sp=0xc0000bd7c8 pc=0x6f41a5 runtime.goexit({}) runtime/asm_amd64.s:1695 +0x1 fp=0xc0000bd7e8 sp=0xc0000bd7e0 pc=0x4742e1 created by net/http.(*connReader).startBackgroundRead in goroutine 50 net/http/server.go:677 +0xba rax 0x0 rbx 0x73ecc6ffd640 rcx 0x73ed1c8969fc rdx 0x6 rdi 0x348c rsi 0x3492 rbp 0x3492 rsp 0x73ecc6ffc150 r8 0x73ecc6ffc220 r9 0x73ecc6ffc1c0 r10 0x8 r11 0x246 r12 0x6 r13 0x16 r14 0x73ecc4475f90 r15 0x73ec97f97440 rip 0x73ed1c8969fc rflags 0x246 cs 0x33 fs 0x0 gs 0x0
GiteaMirror commented 2026-04-12 12:21:13 -05:00
Author
Owner
Copy Link

@FonzieBonzo commented on GitHub (Apr 7, 2024):

In my testing, I did see that forcing the following in the modelfile improved stability:

PARAMETER num_ctx 4196 PARAMETER num_gpu ((Any number greater than 41 worked)

Where can i set this parameters, or is it not posible with the Windows version of Ollama in combination with Open WebUI?

<!-- gh-comment-id:2041367658 --> @FonzieBonzo commented on GitHub (Apr 7, 2024): > In my testing, I did see that forcing the following in the modelfile improved stability: > > PARAMETER num_ctx 4196 PARAMETER num_gpu ((Any number greater than 41 worked) Where can i set this parameters, or is it not posible with the Windows version of Ollama in combination with Open WebUI?
GiteaMirror commented 2026-04-12 12:21:14 -05:00
Author
Owner
Copy Link

@ghost commented on GitHub (Apr 10, 2024):

In my testing, I did see that forcing the following in the modelfile improved stability:

PARAMETER num_ctx 4196 PARAMETER num_gpu ((Any number greater than 41 worked)

Previously it was running well but after some time, started to show same error that :

requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))

then i checked "C:\Users<username>\AppData\Local\Ollama\server.log" file and found following error at end of file:

CUDA error: out of memory
  current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:532
  cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1)
GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error"

then i tried with following solution :
modifying values of num_ctx & num_gpu it resolved

image

but after this it is consuming too much RAM about 90% of my RAM!, but yah its running 👍

image

<!-- gh-comment-id:2046282865 --> @ghost commented on GitHub (Apr 10, 2024): > In my testing, I did see that forcing the following in the modelfile improved stability: > > PARAMETER num_ctx 4196 PARAMETER num_gpu ((Any number greater than 41 worked) Previously it was running well but after some time, started to show same error that : `requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))` then i checked **"C:\Users\<username>\AppData\Local\Ollama\server.log"** file and found following error at end of file: ``` CUDA error: out of memory current device: 0, in function alloc at C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:532 cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1) GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\ggml-cuda.cu:193: !"CUDA error" ``` then i tried with following solution : **modifying values of num_ctx & num_gpu it resolved** ![image](https://github.com/ollama/ollama/assets/166391298/f4ac9247-4f5c-4b1a-a1e7-d2f46cdd0610) but after this it is consuming too much RAM about 90% of my RAM!, but yah its running 👍 ![image](https://github.com/ollama/ollama/assets/166391298/92b24df2-dfee-4ae4-82ce-73aa700b07d3)
GiteaMirror commented 2026-04-12 12:21:14 -05:00
Author
Owner
Copy Link

@systerchristian commented on GitHub (Apr 11, 2024):

Warning. I have had mixed results with my "fix" (which I don't think is actually a fix). Stability is still less than desired. It will frequently get extremely long processing times.

I've moved to KoboldAI for using Command-r. It doesn't seem to have these issues. No clue why, but it works, is faster and stable.

snippet of logs from my last round of testing:(the message it was processing was "Hello there" and a regenerate command)

{"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868661}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1812,"msg":"slot progression","n_past":3,"n_past_se":0,"n_prompt_tokens_processed":44,"slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868661}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":3,"slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868661}
{"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 118765.66 ms / 44 tokens ( 2699.22 ms per token, 0.37 tokens per second)","n_prompt_tokens_processed":44,"n_tokens_second":0.370477465800762,"slot_id":0,"t_prompt_processing":118765.658,"t_token":2699.2194999999997,"task_id":95,"tid":"30204","timestamp":1712868782}
{"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 2988.53 ms / 64 runs ( 46.70 ms per token, 21.42 tokens per second)","n_decoded":64,"n_tokens_second":21.415239485953947,"slot_id":0,"t_token":46.69571875,"t_token_generation":2988.526,"task_id":95,"tid":"30204","timestamp":1712868782}
{"function":"print_timings","level":"INFO","line":295,"msg":" total time = 121754.18 ms","slot_id":0,"t_prompt_processing":118765.658,"t_token_generation":2988.526,"t_total":121754.184,"task_id":95,"tid":"30204","timestamp":1712868782}
{"function":"update_slots","level":"INFO","line":1644,"msg":"slot released","n_cache_tokens":111,"n_ctx":4224,"n_past":110,"n_system_tokens":0,"slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868782,"truncated":false}
[GIN] 2024/04/11 - 13:53:02 | 200 | 2m1s | 192.168.50.32 | POST "/v1/chat/completions"

<!-- gh-comment-id:2050525846 --> @systerchristian commented on GitHub (Apr 11, 2024): Warning. I have had mixed results with my "fix" (which I don't think is actually a fix). Stability is still less than desired. It will frequently get extremely long processing times. I've moved to KoboldAI for using Command-r. It doesn't seem to have these issues. No clue why, but it works, is faster and stable. snippet of logs from my last round of testing:(the message it was processing was "Hello there" and a regenerate command) {"function":"launch_slot_with_data","level":"INFO","line":829,"msg":"slot is processing task","slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868661} {"function":"update_slots","ga_i":0,"level":"INFO","line":1812,"msg":"slot progression","n_past":3,"n_past_se":0,"n_prompt_tokens_processed":44,"slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868661} {"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":3,"slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868661} {"function":"print_timings","level":"INFO","line":272,"msg":"prompt eval time = 118765.66 ms / 44 tokens ( 2699.22 ms per token, 0.37 tokens per second)","n_prompt_tokens_processed":44,"n_tokens_second":0.370477465800762,"slot_id":0,"t_prompt_processing":118765.658,"t_token":2699.2194999999997,"task_id":95,"tid":"30204","timestamp":1712868782} {"function":"print_timings","level":"INFO","line":286,"msg":"generation eval time = 2988.53 ms / 64 runs ( 46.70 ms per token, 21.42 tokens per second)","n_decoded":64,"n_tokens_second":21.415239485953947,"slot_id":0,"t_token":46.69571875,"t_token_generation":2988.526,"task_id":95,"tid":"30204","timestamp":1712868782} {"function":"print_timings","level":"INFO","line":295,"msg":" total time = 121754.18 ms","slot_id":0,"t_prompt_processing":118765.658,"t_token_generation":2988.526,"t_total":121754.184,"task_id":95,"tid":"30204","timestamp":1712868782} {"function":"update_slots","level":"INFO","line":1644,"msg":"slot released","n_cache_tokens":111,"n_ctx":4224,"n_past":110,"n_system_tokens":0,"slot_id":0,"task_id":95,"tid":"30204","timestamp":1712868782,"truncated":false} [GIN] 2024/04/11 - 13:53:02 | 200 | 2m1s | 192.168.50.32 | POST "/v1/chat/completions"
GiteaMirror commented 2026-04-12 12:21:14 -05:00
Author
Owner
Copy Link

@dhiltgen commented on GitHub (Jun 1, 2024):

Please give the latest version a try. We've made improvements in the memory prediction, however there's still some more work to do on Windows VRAM visibility tracked in #4599

<!-- gh-comment-id:2143620225 --> @dhiltgen commented on GitHub (Jun 1, 2024): Please give the latest version a try. We've made improvements in the memory prediction, however there's still some more work to do on Windows VRAM visibility tracked in #4599
GiteaMirror commented 2026-04-12 12:21:14 -05:00
Author
Owner
Copy Link

@dhiltgen commented on GitHub (Jun 22, 2024):

The latest release (0.1.45) has additional fixes that should help. If you're still seeing OOM crashes, please share an updated server log and I'll reopen the issue.

<!-- gh-comment-id:2183596582 --> @dhiltgen commented on GitHub (Jun 22, 2024): The latest release (0.1.45) has additional fixes that should help. If you're still seeing OOM crashes, please share an updated server log and I'll reopen the issue.
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 nvidia
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#2115
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?