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[GH-ISSUE #5071] ollama not utilizing AMD GPU through METAL #49713

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opened 2026-04-28 12:45:57 -05:00 by GiteaMirror · 1 comment
GiteaMirror commented 2026-04-28 12:45:57 -05:00
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Originally created by @dbl001 on GitHub (Jun 15, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/5071

What is the issue?

Here's my build command:

% OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on -DLLAMA_METAL=on -DLLAMA_METAL_EMBED_LIBRARY=on -DGGML_USE_METAL=on -DLLAMA_METAL_COMPILE_SERIALIZED=1" go generate -v ./...

The go script subsequently turns -DLLAMA_METAL=off

+ cmake -S ../llama.cpp -B ../build/darwin/x86_64/cpu_avx2 -DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_METAL_MACOSX_VERSION_MIN=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DLLAMA_METAL_EMBED_LIBRARY=on -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 -DLLAMA_METAL=off -DLLAMA_NATIVE=off -DLLAMA_ACCELERATE=on -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on -DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off

Finally, the server runs without utilizing the GPU.

 % ollama serve
2024/06/15 10:36:43 routes.go:1011: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/Users/davidlaxer/.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: OLLAMA_TMPDIR:]"
time=2024-06-15T10:36:43.742-07:00 level=INFO source=images.go:725 msg="total blobs: 28"
time=2024-06-15T10:36:43.743-07:00 level=INFO source=images.go:732 msg="total unused blobs removed: 0"
time=2024-06-15T10:36:43.744-07:00 level=INFO source=routes.go:1057 msg="Listening on 127.0.0.1:11434 (version 0.1.44)"
time=2024-06-15T10:36:43.744-07:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/ollama2746628305/runners
time=2024-06-15T10:36:43.770-07:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu_avx2 cpu cpu_avx]"
time=2024-06-15T10:36:43.770-07:00 level=INFO source=types.go:71 msg="inference compute" id="" library=cpu compute="" driver=0.0 name="" total="128.0 GiB" available="0 B"
time=2024-06-15T10:41:36.771-07:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=0 memory.available="0 B" memory.required.full="4.6 GiB" memory.required.partial="794.5 MiB" memory.required.kv="256.0 MiB" memory.weights.total="4.1 GiB" memory.weights.repeating="3.7 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB"
time=2024-06-15T10:41:36.772-07:00 level=INFO source=server.go:341 msg="starting llama server" cmd="/var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/ollama2746628305/runners/cpu_avx2/ollama_llama_server --model /Users/davidlaxer/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 63042"
time=2024-06-15T10:41:36.780-07:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-06-15T10:41:36.780-07:00 level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
time=2024-06-15T10:41:36.780-07:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=3051 commit="5921b8f0" tid="0x7ff85e144fc0" timestamp=1718473296
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="0x7ff85e144fc0" timestamp=1718473296 total_threads=16
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="63042" tid="0x7ff85e144fc0" timestamp=1718473296
time=2024-06-15T10:41:37.032-07:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /Users/davidlaxer/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa (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              = Meta-Llama-3-8B-Instruct
llama_model_loader: - kv   2:                          llama.block_count u32              = 32
llama_model_loader: - kv   3:                       llama.context_length u32              = 8192
llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   6:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   7:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv   8:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  10:                          general.file_type u32              = 2
llama_model_loader: - kv  11:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv  21:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type q4_0:  225 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 1.5928 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 128256
llm_load_print_meta: n_merges         = 280147
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_layer          = 32
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            = 4
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             = 14336
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     = linear
llm_load_print_meta: freq_base_train  = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 8192
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 8B
llm_load_print_meta: model ftype      = Q4_0
llm_load_print_meta: model params     = 8.03 B
llm_load_print_meta: model size       = 4.33 GiB (4.64 BPW) 
llm_load_print_meta: general.name     = Meta-Llama-3-8B-Instruct
llm_load_print_meta: BOS token        = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token        = 128009 '<|eot_id|>'
llm_load_print_meta: LF token         = 128 'Ä'
llm_load_print_meta: EOT token        = 128009 '<|eot_id|>'
llm_load_tensors: ggml ctx size =    0.15 MiB
llm_load_tensors:        CPU buffer size =  4437.80 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: flash_attn = 0
llama_new_context_with_model: freq_base  = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:        CPU KV buffer size =   256.00 MiB
llama_new_context_with_model: KV self size  =  256.00 MiB, K (f16):  128.00 MiB, V (f16):  128.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.50 MiB
llama_new_context_with_model:        CPU compute buffer size =   258.50 MiB
llama_new_context_with_model: graph nodes  = 1030
llama_new_context_with_model: graph splits = 1
INFO [main] model loaded | tid="0x7ff85e144fc0" timestamp=1718473304
time=2024-06-15T10:41:44.296-07:00 level=INFO source=server.go:572 msg="llama runner started in 7.52 seconds"
[GIN] 2024/06/15 - 10:41:44 | 200 |  9.093560734s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:41:54 | 200 |  1.154057317s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:42:35 | 200 | 40.688860055s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:42:41 | 200 |  6.229453908s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:42:43 | 200 |  1.270069572s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:43:23 | 200 | 40.445274886s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:43:29 | 200 |   5.92720864s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:43:31 | 200 |  1.186419337s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:44:11 | 200 | 40.475555077s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:44:17 | 200 |  6.143890785s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:44:19 | 200 |  1.327419018s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:44:59 | 200 | 40.358735272s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:45:05 | 200 |  5.842486079s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:45:06 | 200 |  1.151830787s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:45:45 | 200 | 38.130374809s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:45:50 | 200 |  5.863281373s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:45:52 | 200 |  763.567512ms |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:46:16 | 200 | 24.464886509s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:46:17 | 200 |  844.612204ms |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:46:25 | 200 |  7.366777251s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:46:27 | 200 |  1.314771295s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:46:45 | 200 | 18.025285278s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:46:47 | 200 |  1.448278338s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:47:26 | 200 | 38.918308755s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:47:45 | 200 | 18.653427075s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:47:47 | 200 |  1.097321882s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:48:29 | 200 |  41.37452429s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:48:32 | 200 |  1.331141018s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:49:11 | 200 | 39.111446616s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:49:31 | 200 | 20.771630418s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:49:33 | 200 |  1.171729854s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:50:14 | 200 | 40.365819016s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:50:17 | 200 |  1.213320125s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:50:35 | 200 | 18.183581597s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:50:38 | 200 |  1.575906212s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:51:17 | 200 | 39.023216091s |       127.0.0.1 | POST     "/api/embeddings"
[GIN] 2024/06/15 - 10:51:37 | 200 | 19.720073759s |       127.0.0.1 | POST     "/api/embeddings"

If I run

% ./main -m /Users/davidlaxer/llama.cpp/models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1

the AMD GPU is detected

ggml_metal_init: allocating
ggml_metal_init: found device: AMD Radeon Pro 5700 XT
ggml_metal_init: picking default device: AMD Radeon Pro 5700 XT
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/davidlaxer/ollama/llm/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name:   AMD Radeon Pro 5700 XT
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction support   = true
ggml_metal_init: simdgroup matrix mul. support = false
ggml_metal_init: hasUnifiedMemory              = false
ggml_metal_init: recommendedMaxWorkingSetSize  = 17163.09 MB
ggml_metal_init: skipping kernel_mul_mm_f32_f32                    (not supported)
ggml_metal_init: skipping kernel_mul_mm_f16_f32                    (not supported)
ggml_metal_init: skipping kernel_mul_mm_q4_0_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_q4_1_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_q5_0_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_q5_1_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_q8_0_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_q2_K_f32                   (not supported)
...

OS

macOS

GPU

AMD

CPU

Intel

Ollama version

0.2.1

Originally created by @dbl001 on GitHub (Jun 15, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/5071 ### What is the issue? Here's my build command: ``` % OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on -DLLAMA_METAL=on -DLLAMA_METAL_EMBED_LIBRARY=on -DGGML_USE_METAL=on -DLLAMA_METAL_COMPILE_SERIALIZED=1" go generate -v ./... ``` The go script subsequently turns -DLLAMA_METAL=off ``` + cmake -S ../llama.cpp -B ../build/darwin/x86_64/cpu_avx2 -DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_METAL_MACOSX_VERSION_MIN=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DLLAMA_METAL_EMBED_LIBRARY=on -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 -DLLAMA_METAL=off -DLLAMA_NATIVE=off -DLLAMA_ACCELERATE=on -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on -DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off ``` Finally, the server runs without utilizing the GPU. ``` % ollama serve 2024/06/15 10:36:43 routes.go:1011: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/Users/davidlaxer/.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: OLLAMA_TMPDIR:]" time=2024-06-15T10:36:43.742-07:00 level=INFO source=images.go:725 msg="total blobs: 28" time=2024-06-15T10:36:43.743-07:00 level=INFO source=images.go:732 msg="total unused blobs removed: 0" time=2024-06-15T10:36:43.744-07:00 level=INFO source=routes.go:1057 msg="Listening on 127.0.0.1:11434 (version 0.1.44)" time=2024-06-15T10:36:43.744-07:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/ollama2746628305/runners time=2024-06-15T10:36:43.770-07:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu_avx2 cpu cpu_avx]" time=2024-06-15T10:36:43.770-07:00 level=INFO source=types.go:71 msg="inference compute" id="" library=cpu compute="" driver=0.0 name="" total="128.0 GiB" available="0 B" time=2024-06-15T10:41:36.771-07:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=0 memory.available="0 B" memory.required.full="4.6 GiB" memory.required.partial="794.5 MiB" memory.required.kv="256.0 MiB" memory.weights.total="4.1 GiB" memory.weights.repeating="3.7 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB" time=2024-06-15T10:41:36.772-07:00 level=INFO source=server.go:341 msg="starting llama server" cmd="/var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/ollama2746628305/runners/cpu_avx2/ollama_llama_server --model /Users/davidlaxer/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 63042" time=2024-06-15T10:41:36.780-07:00 level=INFO source=sched.go:338 msg="loaded runners" count=1 time=2024-06-15T10:41:36.780-07:00 level=INFO source=server.go:529 msg="waiting for llama runner to start responding" time=2024-06-15T10:41:36.780-07:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=3051 commit="5921b8f0" tid="0x7ff85e144fc0" timestamp=1718473296 INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="0x7ff85e144fc0" timestamp=1718473296 total_threads=16 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="63042" tid="0x7ff85e144fc0" timestamp=1718473296 time=2024-06-15T10:41:37.032-07:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model" llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /Users/davidlaxer/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa (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 = Meta-Llama-3-8B-Instruct llama_model_loader: - kv 2: llama.block_count u32 = 32 llama_model_loader: - kv 3: llama.context_length u32 = 8192 llama_model_loader: - kv 4: llama.embedding_length u32 = 4096 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 6: llama.attention.head_count u32 = 32 llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: general.file_type u32 = 2 llama_model_loader: - kv 11: llama.vocab_size u32 = 128256 llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... llama_model_loader: - kv 21: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q4_0: 225 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens cache size = 256 llm_load_vocab: token to piece cache size = 1.5928 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 128256 llm_load_print_meta: n_merges = 280147 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 32 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 = 4 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 = 14336 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 = linear llm_load_print_meta: freq_base_train = 500000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 8192 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 8B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 8.03 B llm_load_print_meta: model size = 4.33 GiB (4.64 BPW) llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' llm_load_print_meta: EOS token = 128009 '<|eot_id|>' llm_load_print_meta: LF token = 128 'Ä' llm_load_print_meta: EOT token = 128009 '<|eot_id|>' llm_load_tensors: ggml ctx size = 0.15 MiB llm_load_tensors: CPU buffer size = 4437.80 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: flash_attn = 0 llama_new_context_with_model: freq_base = 500000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 256.00 MiB llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB llama_new_context_with_model: CPU output buffer size = 0.50 MiB llama_new_context_with_model: CPU compute buffer size = 258.50 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 1 INFO [main] model loaded | tid="0x7ff85e144fc0" timestamp=1718473304 time=2024-06-15T10:41:44.296-07:00 level=INFO source=server.go:572 msg="llama runner started in 7.52 seconds" [GIN] 2024/06/15 - 10:41:44 | 200 | 9.093560734s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:41:54 | 200 | 1.154057317s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:42:35 | 200 | 40.688860055s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:42:41 | 200 | 6.229453908s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:42:43 | 200 | 1.270069572s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:43:23 | 200 | 40.445274886s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:43:29 | 200 | 5.92720864s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:43:31 | 200 | 1.186419337s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:44:11 | 200 | 40.475555077s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:44:17 | 200 | 6.143890785s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:44:19 | 200 | 1.327419018s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:44:59 | 200 | 40.358735272s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:45:05 | 200 | 5.842486079s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:45:06 | 200 | 1.151830787s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:45:45 | 200 | 38.130374809s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:45:50 | 200 | 5.863281373s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:45:52 | 200 | 763.567512ms | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:46:16 | 200 | 24.464886509s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:46:17 | 200 | 844.612204ms | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:46:25 | 200 | 7.366777251s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:46:27 | 200 | 1.314771295s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:46:45 | 200 | 18.025285278s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:46:47 | 200 | 1.448278338s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:47:26 | 200 | 38.918308755s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:47:45 | 200 | 18.653427075s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:47:47 | 200 | 1.097321882s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:48:29 | 200 | 41.37452429s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:48:32 | 200 | 1.331141018s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:49:11 | 200 | 39.111446616s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:49:31 | 200 | 20.771630418s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:49:33 | 200 | 1.171729854s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:50:14 | 200 | 40.365819016s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:50:17 | 200 | 1.213320125s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:50:35 | 200 | 18.183581597s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:50:38 | 200 | 1.575906212s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:51:17 | 200 | 39.023216091s | 127.0.0.1 | POST "/api/embeddings" [GIN] 2024/06/15 - 10:51:37 | 200 | 19.720073759s | 127.0.0.1 | POST "/api/embeddings" ``` If I run ``` % ./main -m /Users/davidlaxer/llama.cpp/models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1 ``` the AMD GPU is detected ``` ggml_metal_init: allocating ggml_metal_init: found device: AMD Radeon Pro 5700 XT ggml_metal_init: picking default device: AMD Radeon Pro 5700 XT ggml_metal_init: default.metallib not found, loading from source ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil ggml_metal_init: loading '/Users/davidlaxer/ollama/llm/llama.cpp/ggml-metal.metal' ggml_metal_init: GPU name: AMD Radeon Pro 5700 XT ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003) ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001) ggml_metal_init: simdgroup reduction support = true ggml_metal_init: simdgroup matrix mul. support = false ggml_metal_init: hasUnifiedMemory = false ggml_metal_init: recommendedMaxWorkingSetSize = 17163.09 MB ggml_metal_init: skipping kernel_mul_mm_f32_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_f16_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_q4_0_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_q4_1_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_q5_0_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_q5_1_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_q8_0_f32 (not supported) ggml_metal_init: skipping kernel_mul_mm_q2_K_f32 (not supported) ... ``` ### OS macOS ### GPU AMD ### CPU Intel ### Ollama version 0.2.1
GiteaMirror added the bug label 2026-04-28 12:45:57 -05:00
GiteaMirror commented 2026-04-28 12:45:58 -05:00
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@dhiltgen commented on GitHub (Jun 18, 2024):

metal/GPU support for intel macs is being tracked via #1016 and community PRs are welcome.

<!-- gh-comment-id:2176829314 --> @dhiltgen commented on GitHub (Jun 18, 2024): metal/GPU support for intel macs is being tracked via #1016 and community PRs are welcome.
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Reference: github-starred/ollama#49713
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