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[GH-ISSUE #5431] out of memory error when running mixtral:8x22b #29160

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opened 2026-04-22 07:50:44 -05:00 by GiteaMirror · 2 comments
GiteaMirror commented 2026-04-22 07:50:44 -05:00
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Originally created by @Marten-Ka on GitHub (Jul 2, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/5431

What is the issue?

GPU: Nvidia GeForce RTX 2070 (7.5 GB)
RAM: 16 GB

Problem:
I've pulled mixtral:8x22b (ollama pull) and would like to run it. After typing ollama run mixtral:8x22b the process terminates with Error: llama runner process has terminated: exit status 0xc0000409
When looking in the server.log I can see that it fails with the memory error and tells me that it couldn't allocate enough memory.
I've read a lot of issues here and the workaround to limit OLLAMA_MAX_VRAM (limited to 6 GB) didn't help either.

Could you please explain whats the error and how I could handle it? Many thanks in advance!

Log:
2024/07/02 10:51:58 routes.go:1064: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:6000000000 OLLAMA_MODELS:C:\Users\-----\.ollama\models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR:C:\Users\-----\AppData\Local\Programs\Ollama\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-07-02T10:51:58.666+02:00 level=INFO source=images.go:730 msg="total blobs: 5"
time=2024-07-02T10:51:58.668+02:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0"
time=2024-07-02T10:51:58.671+02:00 level=INFO source=routes.go:1111 msg="Listening on 127.0.0.1:11434 (version 0.1.48)"
time=2024-07-02T10:51:58.674+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11.3 rocm_v5.7]"
time=2024-07-02T10:51:58.856+02:00 level=INFO source=types.go:98 msg="inference compute" id=GPU-9b341200-a290-ff84-3a34-412d81b35c4f library=cuda compute=7.5 driver=12.5 name="NVIDIA GeForce RTX 2070 SUPER" total="8.0 GiB" available="7.0 GiB"
[GIN] 2024/07/02 - 10:51:58 | 200 | 0s | 127.0.0.1 | HEAD "/"
[GIN] 2024/07/02 - 10:51:58 | 200 | 21.6534ms | 127.0.0.1 | POST "/api/show"
time=2024-07-02T10:51:58.970+02:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=57 layers.offload=3 layers.split="" memory.available="[7.6 GiB]" memory.required.full="77.5 GiB" memory.required.partial="7.1 GiB" memory.required.kv="448.0 MiB" memory.required.allocations="[7.1 GiB]" memory.weights.total="74.2 GiB" memory.weights.repeating="74.1 GiB" memory.weights.nonrepeating="157.5 MiB" memory.graph.full="244.0 MiB" memory.graph.partial="1.3 GiB"
time=2024-07-02T10:51:58.981+02:00 level=INFO source=server.go:368 msg="starting llama server" cmd="C:\Users\-----\AppData\Local\Programs\Ollama\ollama_runners\cuda_v11.3\ollama_llama_server.exe --model C:\Users\-----\.ollama\models\blobs\sha256-d0eeef8264ce10a7e578789ee69986c66425639e72c9855e36a0345c230918c9 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 3 --no-mmap --parallel 1 --port 51455"
time=2024-07-02T10:51:59.007+02:00 level=INFO source=sched.go:382 msg="loaded runners" count=1
time=2024-07-02T10:51:59.007+02:00 level=INFO source=server.go:556 msg="waiting for llama runner to start responding"
time=2024-07-02T10:51:59.008+02:00 level=INFO source=server.go:594 msg="waiting for server to become available" status="llm server error"
INFO [wmain] build info | build=3171 commit="7c26775a" tid="6888" timestamp=1719910319
INFO [wmain] system info | n_threads=4 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="6888" timestamp=1719910319 total_threads=8
INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="51455" tid="6888" timestamp=1719910319
llama_model_loader: loaded meta data with 28 key-value pairs and 563 tensors from C:\Users-----.ollama\models\blobs\sha256-d0eeef8264ce10a7e578789ee69986c66425639e72c9855e36a0345c230918c9 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = Mixtral-8x22B-Instruct-v0.1
llama_model_loader: - kv 2: llama.block_count u32 = 56
llama_model_loader: - kv 3: llama.context_length u32 = 65536
llama_model_loader: - kv 4: llama.embedding_length u32 = 6144
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: llama.attention.head_count u32 = 48
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.expert_count u32 = 8
llama_model_loader: - kv 11: llama.expert_used_count u32 = 2
llama_model_loader: - kv 12: general.file_type u32 = 2
llama_model_loader: - kv 13: llama.vocab_size u32 = 32768
llama_model_loader: - kv 14: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 15: tokenizer.ggml.model str = llama
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,32768] = ["", "", "", "[INST]", "[...
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,32768] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32768] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template.tool_use str = {{bos_token}}{% set user_messages = m...
llama_model_loader: - kv 25: tokenizer.chat_templates arr[str,1] = ["tool_use"]
llama_model_loader: - kv 26: tokenizer.chat_template str = {{bos_token}}{% for message in messag...
llama_model_loader: - kv 27: general.quantization_version u32 = 2
llama_model_loader: - type f32: 113 tensors
llama_model_loader: - type f16: 56 tensors
llama_model_loader: - type q4_0: 281 tensors
llama_model_loader: - type q8_0: 112 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens cache size = 259
llm_load_vocab: token to piece cache size = 0.1732 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32768
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 65536
llm_load_print_meta: n_embd = 6144
llm_load_print_meta: n_head = 48
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 56
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 = 6
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 16384
llm_load_print_meta: n_expert = 8
llm_load_print_meta: n_expert_used = 2
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 = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 65536
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 = 8x22B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 140.63 B
llm_load_print_meta: model size = 74.05 GiB (4.52 BPW)
llm_load_print_meta: general.name = Mixtral-8x22B-Instruct-v0.1
llm_load_print_meta: BOS token = 1 ''
llm_load_print_meta: EOS token = 2 ''
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 781 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 2070 SUPER, compute capability 7.5, VMM: yes
llm_load_tensors: ggml ctx size = 0.56 MiB
ggml_cuda_host_malloc: failed to allocate 71783.23 MiB of pinned memory: out of memory
ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 75270168608
llama_model_load: error loading model: unable to allocate backend buffer
llama_load_model_from_file: exception loading model
time=2024-07-02T10:51:59.625+02:00 level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000409 "
[GIN] 2024/07/02 - 10:51:59 | 500 | 707.5768ms | 127.0.0.1 | POST "/api/chat"

OS

Windows

GPU

Nvidia

CPU

Intel

Ollama version

0.1.48

Originally created by @Marten-Ka on GitHub (Jul 2, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/5431 ### What is the issue? **GPU:** Nvidia GeForce RTX 2070 (7.5 GB) **RAM:** 16 GB **Problem:** I've pulled mixtral:8x22b (`ollama pull`) and would like to run it. After typing `ollama run mixtral:8x22b` the process terminates with Error: llama runner process has terminated: exit status 0xc0000409 When looking in the server.log I can see that it fails with the memory error and tells me that it couldn't allocate enough memory. I've read a lot of issues here and the workaround to limit OLLAMA_MAX_VRAM (limited to 6 GB) didn't help either. Could you please explain whats the error and how I could handle it? Many thanks in advance! **Log:** 2024/07/02 10:51:58 routes.go:1064: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:6000000000 OLLAMA_MODELS:C:\\Users\\-----\\.ollama\\models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR:C:\\Users\\-----\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-07-02T10:51:58.666+02:00 level=INFO source=images.go:730 msg="total blobs: 5" time=2024-07-02T10:51:58.668+02:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0" time=2024-07-02T10:51:58.671+02:00 level=INFO source=routes.go:1111 msg="Listening on 127.0.0.1:11434 (version 0.1.48)" time=2024-07-02T10:51:58.674+02:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11.3 rocm_v5.7]" time=2024-07-02T10:51:58.856+02:00 level=INFO source=types.go:98 msg="inference compute" id=GPU-9b341200-a290-ff84-3a34-412d81b35c4f library=cuda compute=7.5 driver=12.5 name="NVIDIA GeForce RTX 2070 SUPER" total="8.0 GiB" available="7.0 GiB" [GIN] 2024/07/02 - 10:51:58 | 200 | 0s | 127.0.0.1 | HEAD "/" [GIN] 2024/07/02 - 10:51:58 | 200 | 21.6534ms | 127.0.0.1 | POST "/api/show" time=2024-07-02T10:51:58.970+02:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=57 layers.offload=3 layers.split="" memory.available="[7.6 GiB]" memory.required.full="77.5 GiB" memory.required.partial="7.1 GiB" memory.required.kv="448.0 MiB" memory.required.allocations="[7.1 GiB]" memory.weights.total="74.2 GiB" memory.weights.repeating="74.1 GiB" memory.weights.nonrepeating="157.5 MiB" memory.graph.full="244.0 MiB" memory.graph.partial="1.3 GiB" time=2024-07-02T10:51:58.981+02:00 level=INFO source=server.go:368 msg="starting llama server" cmd="C:\\Users\\-----\\AppData\\Local\\Programs\\Ollama\\ollama_runners\\cuda_v11.3\\ollama_llama_server.exe --model C:\\Users\\-----\\.ollama\\models\\blobs\\sha256-d0eeef8264ce10a7e578789ee69986c66425639e72c9855e36a0345c230918c9 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 3 --no-mmap --parallel 1 --port 51455" time=2024-07-02T10:51:59.007+02:00 level=INFO source=sched.go:382 msg="loaded runners" count=1 time=2024-07-02T10:51:59.007+02:00 level=INFO source=server.go:556 msg="waiting for llama runner to start responding" time=2024-07-02T10:51:59.008+02:00 level=INFO source=server.go:594 msg="waiting for server to become available" status="llm server error" INFO [wmain] build info | build=3171 commit="7c26775a" tid="6888" timestamp=1719910319 INFO [wmain] system info | n_threads=4 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="6888" timestamp=1719910319 total_threads=8 INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="51455" tid="6888" timestamp=1719910319 llama_model_loader: loaded meta data with 28 key-value pairs and 563 tensors from C:\Users\-----\.ollama\models\blobs\sha256-d0eeef8264ce10a7e578789ee69986c66425639e72c9855e36a0345c230918c9 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = Mixtral-8x22B-Instruct-v0.1 llama_model_loader: - kv 2: llama.block_count u32 = 56 llama_model_loader: - kv 3: llama.context_length u32 = 65536 llama_model_loader: - kv 4: llama.embedding_length u32 = 6144 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 16384 llama_model_loader: - kv 6: llama.attention.head_count u32 = 48 llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 8: llama.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: llama.expert_count u32 = 8 llama_model_loader: - kv 11: llama.expert_used_count u32 = 2 llama_model_loader: - kv 12: general.file_type u32 = 2 llama_model_loader: - kv 13: llama.vocab_size u32 = 32768 llama_model_loader: - kv 14: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 15: tokenizer.ggml.model str = llama llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,32768] = ["<unk>", "<s>", "</s>", "[INST]", "[... llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,32768] = [-1000.000000, -1000.000000, -1000.00... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32768] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 24: tokenizer.chat_template.tool_use str = {{bos_token}}{% set user_messages = m... llama_model_loader: - kv 25: tokenizer.chat_templates arr[str,1] = ["tool_use"] llama_model_loader: - kv 26: tokenizer.chat_template str = {{bos_token}}{% for message in messag... llama_model_loader: - kv 27: general.quantization_version u32 = 2 llama_model_loader: - type f32: 113 tensors llama_model_loader: - type f16: 56 tensors llama_model_loader: - type q4_0: 281 tensors llama_model_loader: - type q8_0: 112 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens cache size = 259 llm_load_vocab: token to piece cache size = 0.1732 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32768 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 65536 llm_load_print_meta: n_embd = 6144 llm_load_print_meta: n_head = 48 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 56 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 = 6 llm_load_print_meta: n_embd_k_gqa = 1024 llm_load_print_meta: n_embd_v_gqa = 1024 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 16384 llm_load_print_meta: n_expert = 8 llm_load_print_meta: n_expert_used = 2 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 = linear llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 65536 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 = 8x22B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 140.63 B llm_load_print_meta: model size = 74.05 GiB (4.52 BPW) llm_load_print_meta: general.name = Mixtral-8x22B-Instruct-v0.1 llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 781 '<0x0A>' ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 2070 SUPER, compute capability 7.5, VMM: yes llm_load_tensors: ggml ctx size = 0.56 MiB ggml_cuda_host_malloc: failed to allocate 71783.23 MiB of pinned memory: out of memory ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 75270168608 llama_model_load: error loading model: unable to allocate backend buffer llama_load_model_from_file: exception loading model time=2024-07-02T10:51:59.625+02:00 level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000409 " [GIN] 2024/07/02 - 10:51:59 | 500 | 707.5768ms | 127.0.0.1 | POST "/api/chat" ### OS Windows ### GPU Nvidia ### CPU Intel ### Ollama version 0.1.48
GiteaMirror added the bug label 2026-04-22 07:50:44 -05:00
GiteaMirror commented 2026-04-22 07:50:46 -05:00
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@bearjaws commented on GitHub (Jul 2, 2024):

I believe you need a much smaller quantization to run that model. IIRC the default 8x22 from ollama run mixtral:8x22b needs 32GiB combined vram+system memory to make it work, so at 22GiB you are not going to be able to run it.

You could try a Q2 version but the quality is going to go down significantly. Does something smaller like llama3 work without issue?

<!-- gh-comment-id:2203036120 --> @bearjaws commented on GitHub (Jul 2, 2024): I believe you need a much smaller quantization to run that model. IIRC the default 8x22 from `ollama run mixtral:8x22b` needs 32GiB combined vram+system memory to make it work, so at 22GiB you are not going to be able to run it. You could try a Q2 version but the quality is going to go down significantly. Does something smaller like llama3 work without issue?
GiteaMirror commented 2026-04-22 07:50:47 -05:00
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@dhiltgen commented on GitHub (Jul 2, 2024):

This is a dup of #4955 - you're attempting to load a model that requires ~77G on a system that has a total of ~23G (VRAM + system memory) which wont work. Once we resolve that issue we'll report a better error message instead of trying to load the model and failing. As @bearjaws mentioned above, you'll need to try a smaller model on your system, ideally one that fits in the ~7G of VRAM for optimal performance.

<!-- gh-comment-id:2204398444 --> @dhiltgen commented on GitHub (Jul 2, 2024): This is a dup of #4955 - you're attempting to load a model that requires ~77G on a system that has a total of ~23G (VRAM + system memory) which wont work. Once we resolve that issue we'll report a better error message instead of trying to load the model and failing. As @bearjaws mentioned above, you'll need to try a smaller model on your system, ideally one that fits in the ~7G of VRAM for optimal performance.
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