github-starred/ollama
2
0
Fork 0
mirror of https://github.com/ollama/ollama.git synced 2026-07-16 13:19:40 -05:00
Code Issues 16.9k Packages Projects Releases 122 Wiki Activity

[GH-ISSUE #2950] When I access it through the API, ollama crashes with an 'out of memory' error,while I use the gemma-7b model. but it works fine when I use 'ollama run gemma' in Terminal #1811

New Issue
Closed
opened 2026-04-12 11:51:31 -05:00 by GiteaMirror · 3 comments
GiteaMirror commented 2026-04-12 11:51:31 -05:00
Owner
Copy Link

Originally created by @panp1 on GitHub (Mar 6, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/2950

[GIN] 2024/03/06 - 17:37:31 | 200 | 11.9573683s | ::1 | POST "/api/chat"
[GIN] 2024/03/06 - 17:43:04 | 200 | 11.4739ms | ::1 | GET "/api/tags"
time=2024-03-06T17:43:13.345+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-06T17:43:13.345+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9"
time=2024-03-06T17:43:13.345+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-03-06T17:43:13.345+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9"
time=2024-03-06T17:43:13.345+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
loading library C:\Users\Paddy\AppData\Local\Temp\ollama1734183266\cuda_v11.3\ext_server.dll
time=2024-03-06T17:43:13.349+08:00 level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: C:\Users\Paddy\AppData\Local\Temp\ollama1734183266\cuda_v11.3\ext_server.dll"
time=2024-03-06T17:43:13.349+08:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server"
llama_model_loader: loaded meta data with 24 key-value pairs and 254 tensors from F:\ollama\blobs\sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = gemma
llama_model_loader: - kv 1: general.name str = gemma-7b-it
llama_model_loader: - kv 2: gemma.context_length u32 = 8192
llama_model_loader: - kv 3: gemma.embedding_length u32 = 3072
llama_model_loader: - kv 4: gemma.block_count u32 = 28
llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 24576
llama_model_loader: - kv 6: gemma.attention.head_count u32 = 16
llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 16
llama_model_loader: - kv 8: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 9: gemma.attention.key_length u32 = 256
llama_model_loader: - kv 10: gemma.attention.value_length u32 = 256
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,256000] = ["", "", "", "", ...
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,256000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 21: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'...
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - kv 23: general.file_type u32 = 2
llama_model_loader: - type f32: 57 tensors
llama_model_loader: - type q4_0: 196 tensors
llama_model_loader: - type q8_0: 1 tensors
llm_load_vocab: mismatch in special tokens definition ( 416/256000 vs 260/256000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = gemma
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 256000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 3072
llm_load_print_meta: n_head = 16
llm_load_print_meta: n_head_kv = 16
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_rot = 192
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 24576
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 = 7B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 8.54 B
llm_load_print_meta: model size = 4.84 GiB (4.87 BPW)
llm_load_print_meta: general.name = gemma-7b-it
llm_load_print_meta: BOS token = 2 ''
llm_load_print_meta: EOS token = 1 ''
llm_load_print_meta: UNK token = 3 ''
llm_load_print_meta: PAD token = 0 ''
llm_load_print_meta: LF token = 227 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.19 MiB
llm_load_tensors: offloading 28 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 29/29 layers to GPU
llm_load_tensors: CPU buffer size = 796.88 MiB
llm_load_tensors: CUDA0 buffer size = 4955.54 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
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4060 Laptop GPU, compute capability 8.9, VMM: yes
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 input buffer size = 11.02 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 506.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 6.00 MiB
llama_new_context_with_model: graph splits (measure): 2
{"function":"initialize","level":"INFO","line":433,"msg":"initializing slots","n_slots":1,"tid":"6404","timestamp":1709718196}
{"function":"initialize","level":"INFO","line":445,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"6404","timestamp":1709718196}
time=2024-03-06T17:43:16.485+08:00 level=INFO source=dyn_ext_server.go:161 msg="Starting llama main loop"
{"function":"update_slots","level":"INFO","line":1565,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"25596","timestamp":1709718196}
{"function":"launch_slot_with_data","level":"INFO","line":826,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"25596","timestamp":1709718196}
{"function":"update_slots","level":"INFO","line":1801,"msg":"slot progression","n_past":0,"n_prompt_tokens_processed":978,"slot_id":0,"task_id":0,"tid":"25596","timestamp":1709718196}
{"function":"update_slots","level":"INFO","line":1825,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"25596","timestamp":1709718196}
CUDA error: out of memory
current device: 0, in function ggml_cuda_pool_malloc_vmm at C:\Users\jmorg\git\ollama\llm\llama.cpp\ggml-cuda.cu:8601
cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1)
GGML_ASSERT: C:\Users\jmorg\git\ollama\llm\llama.cpp\ggml-cuda.cu:256: !"CUDA error"

Wed Mar 6 17:46:57 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 551.61 Driver Version: 551.61 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 4060 ... WDDM | 00000000:01:00.0 Off | N/A |
| N/A 38C P0 20W / 115W | 0MiB / 8188MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+

Originally created by @panp1 on GitHub (Mar 6, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/2950 [GIN] 2024/03/06 - 17:37:31 | 200 | 11.9573683s | ::1 | POST "/api/chat" [GIN] 2024/03/06 - 17:43:04 | 200 | 11.4739ms | ::1 | GET "/api/tags" time=2024-03-06T17:43:13.345+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-03-06T17:43:13.345+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9" time=2024-03-06T17:43:13.345+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-03-06T17:43:13.345+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.9" time=2024-03-06T17:43:13.345+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" loading library C:\Users\Paddy\AppData\Local\Temp\ollama1734183266\cuda_v11.3\ext_server.dll time=2024-03-06T17:43:13.349+08:00 level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: C:\\Users\\Paddy\\AppData\\Local\\Temp\\ollama1734183266\\cuda_v11.3\\ext_server.dll" time=2024-03-06T17:43:13.349+08:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server" llama_model_loader: loaded meta data with 24 key-value pairs and 254 tensors from F:\ollama\blobs\sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = gemma llama_model_loader: - kv 1: general.name str = gemma-7b-it llama_model_loader: - kv 2: gemma.context_length u32 = 8192 llama_model_loader: - kv 3: gemma.embedding_length u32 = 3072 llama_model_loader: - kv 4: gemma.block_count u32 = 28 llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 24576 llama_model_loader: - kv 6: gemma.attention.head_count u32 = 16 llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 16 llama_model_loader: - kv 8: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 9: gemma.attention.key_length u32 = 256 llama_model_loader: - kv 10: gemma.attention.value_length u32 = 256 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,256000] = ["<pad>", "<eos>", "<bos>", "<unk>", ... llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,256000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,256000] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 2 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 3 llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 21: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'... llama_model_loader: - kv 22: general.quantization_version u32 = 2 llama_model_loader: - kv 23: general.file_type u32 = 2 llama_model_loader: - type f32: 57 tensors llama_model_loader: - type q4_0: 196 tensors llama_model_loader: - type q8_0: 1 tensors llm_load_vocab: mismatch in special tokens definition ( 416/256000 vs 260/256000 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = gemma llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 256000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 3072 llm_load_print_meta: n_head = 16 llm_load_print_meta: n_head_kv = 16 llm_load_print_meta: n_layer = 28 llm_load_print_meta: n_rot = 192 llm_load_print_meta: n_embd_head_k = 256 llm_load_print_meta: n_embd_head_v = 256 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 4096 llm_load_print_meta: n_embd_v_gqa = 4096 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: n_ff = 24576 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 = 7B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 8.54 B llm_load_print_meta: model size = 4.84 GiB (4.87 BPW) llm_load_print_meta: general.name = gemma-7b-it llm_load_print_meta: BOS token = 2 '<bos>' llm_load_print_meta: EOS token = 1 '<eos>' llm_load_print_meta: UNK token = 3 '<unk>' llm_load_print_meta: PAD token = 0 '<pad>' llm_load_print_meta: LF token = 227 '<0x0A>' llm_load_tensors: ggml ctx size = 0.19 MiB llm_load_tensors: offloading 28 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 29/29 layers to GPU llm_load_tensors: CPU buffer size = 796.88 MiB llm_load_tensors: CUDA0 buffer size = 4955.54 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 ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 4060 Laptop GPU, compute capability 8.9, VMM: yes 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 input buffer size = 11.02 MiB llama_new_context_with_model: CUDA0 compute buffer size = 506.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 6.00 MiB llama_new_context_with_model: graph splits (measure): 2 {"function":"initialize","level":"INFO","line":433,"msg":"initializing slots","n_slots":1,"tid":"6404","timestamp":1709718196} {"function":"initialize","level":"INFO","line":445,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"6404","timestamp":1709718196} time=2024-03-06T17:43:16.485+08:00 level=INFO source=dyn_ext_server.go:161 msg="Starting llama main loop" {"function":"update_slots","level":"INFO","line":1565,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"25596","timestamp":1709718196} {"function":"launch_slot_with_data","level":"INFO","line":826,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"25596","timestamp":1709718196} {"function":"update_slots","level":"INFO","line":1801,"msg":"slot progression","n_past":0,"n_prompt_tokens_processed":978,"slot_id":0,"task_id":0,"tid":"25596","timestamp":1709718196} {"function":"update_slots","level":"INFO","line":1825,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"25596","timestamp":1709718196} CUDA error: out of memory current device: 0, in function ggml_cuda_pool_malloc_vmm at C:\Users\jmorg\git\ollama\llm\llama.cpp\ggml-cuda.cu:8601 cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1) GGML_ASSERT: C:\Users\jmorg\git\ollama\llm\llama.cpp\ggml-cuda.cu:256: !"CUDA error" Wed Mar 6 17:46:57 2024 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 551.61 Driver Version: 551.61 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 4060 ... WDDM | 00000000:01:00.0 Off | N/A | | N/A 38C P0 20W / 115W | 0MiB / 8188MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | No running processes found | +-----------------------------------------------------------------------------------------+
GiteaMirror commented 2026-04-12 11:51:33 -05:00
Author
Owner
Copy Link

@thomasWos commented on GitHub (Mar 6, 2024):

Probably similar to https://github.com/ollama/ollama/issues/1952

<!-- gh-comment-id:1980552800 --> @thomasWos commented on GitHub (Mar 6, 2024): Probably similar to https://github.com/ollama/ollama/issues/1952
GiteaMirror commented 2026-04-12 11:51:34 -05:00
Author
Owner
Copy Link

@panp1 commented on GitHub (Mar 7, 2024):

Try with NVIDIA GeForce RTX 3060 6G works fine. But not work NVIDIA GeForce RTX 4060 8G. it's weird.

<!-- gh-comment-id:1982164593 --> @panp1 commented on GitHub (Mar 7, 2024): Try with NVIDIA GeForce RTX 3060 6G works fine. But not work NVIDIA GeForce RTX 4060 8G. it's weird.
GiteaMirror commented 2026-04-12 11:51:34 -05:00
Author
Owner
Copy Link

@jmorganca commented on GitHub (Mar 12, 2024):

Hi all, this is indeed related to https://github.com/ollama/ollama/issues/1952 – will close for this and sorry about the errors.

<!-- gh-comment-id:1989732823 --> @jmorganca commented on GitHub (Mar 12, 2024): Hi all, this is indeed related to https://github.com/ollama/ollama/issues/1952 – will close for this and sorry about the errors.
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
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#1811
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?