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[GH-ISSUE #5387] Intel Integrated Graphics GPU not being utilized when OLLAMA_INTEL_GPU flag is enabled #3368

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opened 2026-04-12 13:59:46 -05:00 by GiteaMirror · 4 comments
GiteaMirror commented 2026-04-12 13:59:46 -05:00
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Originally created by @suncloudsmoon on GitHub (Jun 30, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/5387

What is the issue?

When the flag 'OLLAMA_INTEL_GPU' is enabled, I expect Ollama to take full advantage of the Intel GPU/iGPU present on the system. However, the intel iGPU is not utilized at all on my system. My Intel iGPU is Intel Iris Xe Graphics (11th gen).
Logs:

C:\Users\ocean>ollama serve
2024/06/29 17:35:53 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:true OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:C:\\Users\\ocean\\.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\\ocean\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-06-29T17:35:53.939-07:00 level=INFO source=images.go:730 msg="total blobs: 23"
time=2024-06-29T17:35:53.941-07:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0"
time=2024-06-29T17:35:53.943-07:00 level=INFO source=routes.go:1111 msg="Listening on 127.0.0.1:11434 (version 0.1.48)"
time=2024-06-29T17:35:53.943-07:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11.3 rocm_v5.7]"
time=2024-06-29T17:35:54.605-07:00 level=INFO source=types.go:98 msg="inference compute" id=0 library=oneapi compute="" driver=0.0 name="" total="0 B" available="0 B"
[GIN] 2024/06/29 - 17:36:34 | 200 |     62.0367ms |       127.0.0.1 | GET      "/api/tags"
[GIN] 2024/06/29 - 17:36:35 | 200 |      4.8491ms |       127.0.0.1 | GET      "/api/tags"
[GIN] 2024/06/29 - 17:36:36 | 200 |       533.5µs |       127.0.0.1 | GET      "/api/version"
time=2024-06-29T17:36:58.350-07:00 level=INFO source=memory.go:309 msg="offload to oneapi" layers.requested=-1 layers.model=29 layers.offload=0 layers.split="" memory.available="[0 B]" memory.required.full="1.0 GiB" memory.required.partial="0 B" memory.required.kv="56.0 MiB" memory.required.allocations="[0 B]" memory.weights.total="808.1 MiB" memory.weights.repeating="625.5 MiB" memory.weights.nonrepeating="182.6 MiB" memory.graph.full="299.8 MiB" memory.graph.partial="482.3 MiB"
time=2024-06-29T17:36:58.375-07:00 level=INFO source=server.go:368 msg="starting llama server" cmd="C:\\Users\\ocean\\AppData\\Local\\Programs\\Ollama\\ollama_runners\\cpu_avx2\\ollama_llama_server.exe --model C:\\Users\\ocean\\.ollama\\models\\blobs\\sha256-6acb9bb78ee9d70d4d210ebc3903e6719c7ddb9796dd120f962f640530813603 --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 52261"
time=2024-06-29T17:36:58.434-07:00 level=INFO source=sched.go:382 msg="loaded runners" count=1
time=2024-06-29T17:36:58.434-07:00 level=INFO source=server.go:556 msg="waiting for llama runner to start responding"
time=2024-06-29T17:36:58.435-07:00 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="33680" timestamp=1719707818
INFO [wmain] system info | n_threads=4 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 = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="33680" timestamp=1719707818 total_threads=8
INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="52261" tid="33680" timestamp=1719707818
llama_model_loader: loaded meta data with 21 key-value pairs and 338 tensors from C:\Users\ocean\.ollama\models\blobs\sha256-6acb9bb78ee9d70d4d210ebc3903e6719c7ddb9796dd120f962f640530813603 (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              = qwen2
llama_model_loader: - kv   1:                               general.name str              = Qwen2-1.5B-Instruct
llama_model_loader: - kv   2:                          qwen2.block_count u32              = 28
llama_model_loader: - kv   3:                       qwen2.context_length u32              = 32768
llama_model_loader: - kv   4:                     qwen2.embedding_length u32              = 1536
llama_model_loader: - kv   5:                  qwen2.feed_forward_length u32              = 8960
llama_model_loader: - kv   6:                 qwen2.attention.head_count u32              = 12
llama_model_loader: - kv   7:              qwen2.attention.head_count_kv u32              = 2
llama_model_loader: - kv   8:                       qwen2.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv   9:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  10:                          general.file_type u32              = 15
llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  12:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  15:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  17:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  19:                    tokenizer.chat_template str              = {% for message in messages %}{% if lo...
llama_model_loader: - kv  20:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  141 tensors
llama_model_loader: - type q4_K:  168 tensors
llama_model_loader: - type q6_K:   29 tensors
time=2024-06-29T17:36:58.689-07:00 level=INFO source=server.go:594 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 293
llm_load_vocab: token to piece cache size = 0.9338 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = qwen2
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 151936
llm_load_print_meta: n_merges         = 151387
llm_load_print_meta: n_ctx_train      = 32768
llm_load_print_meta: n_embd           = 1536
llm_load_print_meta: n_head           = 12
llm_load_print_meta: n_head_kv        = 2
llm_load_print_meta: n_layer          = 28
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     = 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: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 8960
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 2
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 32768
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       = ?B
llm_load_print_meta: model ftype      = Q4_K - Medium
llm_load_print_meta: model params     = 1.54 B
llm_load_print_meta: model size       = 934.69 MiB (5.08 BPW)
llm_load_print_meta: general.name     = Qwen2-1.5B-Instruct
llm_load_print_meta: BOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token        = 151645 '<|im_end|>'
llm_load_print_meta: PAD token        = 151643 '<|endoftext|>'
llm_load_print_meta: LF token         = 148848 'ÄĬ'
llm_load_print_meta: EOT token        = 151645 '<|im_end|>'
llm_load_tensors: ggml ctx size =    0.16 MiB
llm_load_tensors:        CPU buffer size =   934.69 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  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:        CPU KV buffer size =    56.00 MiB
llama_new_context_with_model: KV self size  =   56.00 MiB, K (f16):   28.00 MiB, V (f16):   28.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.59 MiB
llama_new_context_with_model:        CPU compute buffer size =   299.75 MiB
llama_new_context_with_model: graph nodes  = 986
llama_new_context_with_model: graph splits = 1
INFO [wmain] model loaded | tid="33680" timestamp=1719707819
time=2024-06-29T17:36:59.739-07:00 level=INFO source=server.go:599 msg="llama runner started in 1.31 seconds"

OS

Windows

GPU

Intel

CPU

Intel

Ollama version

0.1.48

Originally created by @suncloudsmoon on GitHub (Jun 30, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/5387 ### What is the issue? When the flag 'OLLAMA_INTEL_GPU' is enabled, I expect Ollama to take full advantage of the Intel GPU/iGPU present on the system. However, the intel iGPU is not utilized at all on my system. My Intel iGPU is Intel Iris Xe Graphics (11th gen). Logs: ``` C:\Users\ocean>ollama serve 2024/06/29 17:35:53 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:true OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:C:\\Users\\ocean\\.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\\ocean\\AppData\\Local\\Programs\\Ollama\\ollama_runners OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-06-29T17:35:53.939-07:00 level=INFO source=images.go:730 msg="total blobs: 23" time=2024-06-29T17:35:53.941-07:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0" time=2024-06-29T17:35:53.943-07:00 level=INFO source=routes.go:1111 msg="Listening on 127.0.0.1:11434 (version 0.1.48)" time=2024-06-29T17:35:53.943-07:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11.3 rocm_v5.7]" time=2024-06-29T17:35:54.605-07:00 level=INFO source=types.go:98 msg="inference compute" id=0 library=oneapi compute="" driver=0.0 name="" total="0 B" available="0 B" [GIN] 2024/06/29 - 17:36:34 | 200 | 62.0367ms | 127.0.0.1 | GET "/api/tags" [GIN] 2024/06/29 - 17:36:35 | 200 | 4.8491ms | 127.0.0.1 | GET "/api/tags" [GIN] 2024/06/29 - 17:36:36 | 200 | 533.5µs | 127.0.0.1 | GET "/api/version" time=2024-06-29T17:36:58.350-07:00 level=INFO source=memory.go:309 msg="offload to oneapi" layers.requested=-1 layers.model=29 layers.offload=0 layers.split="" memory.available="[0 B]" memory.required.full="1.0 GiB" memory.required.partial="0 B" memory.required.kv="56.0 MiB" memory.required.allocations="[0 B]" memory.weights.total="808.1 MiB" memory.weights.repeating="625.5 MiB" memory.weights.nonrepeating="182.6 MiB" memory.graph.full="299.8 MiB" memory.graph.partial="482.3 MiB" time=2024-06-29T17:36:58.375-07:00 level=INFO source=server.go:368 msg="starting llama server" cmd="C:\\Users\\ocean\\AppData\\Local\\Programs\\Ollama\\ollama_runners\\cpu_avx2\\ollama_llama_server.exe --model C:\\Users\\ocean\\.ollama\\models\\blobs\\sha256-6acb9bb78ee9d70d4d210ebc3903e6719c7ddb9796dd120f962f640530813603 --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 52261" time=2024-06-29T17:36:58.434-07:00 level=INFO source=sched.go:382 msg="loaded runners" count=1 time=2024-06-29T17:36:58.434-07:00 level=INFO source=server.go:556 msg="waiting for llama runner to start responding" time=2024-06-29T17:36:58.435-07:00 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="33680" timestamp=1719707818 INFO [wmain] system info | n_threads=4 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 = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="33680" timestamp=1719707818 total_threads=8 INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="52261" tid="33680" timestamp=1719707818 llama_model_loader: loaded meta data with 21 key-value pairs and 338 tensors from C:\Users\ocean\.ollama\models\blobs\sha256-6acb9bb78ee9d70d4d210ebc3903e6719c7ddb9796dd120f962f640530813603 (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 = qwen2 llama_model_loader: - kv 1: general.name str = Qwen2-1.5B-Instruct llama_model_loader: - kv 2: qwen2.block_count u32 = 28 llama_model_loader: - kv 3: qwen2.context_length u32 = 32768 llama_model_loader: - kv 4: qwen2.embedding_length u32 = 1536 llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 8960 llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 12 llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 2 llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 10: general.file_type u32 = 15 llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo... llama_model_loader: - kv 20: general.quantization_version u32 = 2 llama_model_loader: - type f32: 141 tensors llama_model_loader: - type q4_K: 168 tensors llama_model_loader: - type q6_K: 29 tensors time=2024-06-29T17:36:58.689-07:00 level=INFO source=server.go:594 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: special tokens cache size = 293 llm_load_vocab: token to piece cache size = 0.9338 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = qwen2 llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 151936 llm_load_print_meta: n_merges = 151387 llm_load_print_meta: n_ctx_train = 32768 llm_load_print_meta: n_embd = 1536 llm_load_print_meta: n_head = 12 llm_load_print_meta: n_head_kv = 2 llm_load_print_meta: n_layer = 28 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 = 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: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 8960 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 32768 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 = ?B llm_load_print_meta: model ftype = Q4_K - Medium llm_load_print_meta: model params = 1.54 B llm_load_print_meta: model size = 934.69 MiB (5.08 BPW) llm_load_print_meta: general.name = Qwen2-1.5B-Instruct llm_load_print_meta: BOS token = 151643 '<|endoftext|>' llm_load_print_meta: EOS token = 151645 '<|im_end|>' llm_load_print_meta: PAD token = 151643 '<|endoftext|>' llm_load_print_meta: LF token = 148848 'ÄĬ' llm_load_print_meta: EOT token = 151645 '<|im_end|>' llm_load_tensors: ggml ctx size = 0.16 MiB llm_load_tensors: CPU buffer size = 934.69 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 = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 56.00 MiB llama_new_context_with_model: KV self size = 56.00 MiB, K (f16): 28.00 MiB, V (f16): 28.00 MiB llama_new_context_with_model: CPU output buffer size = 0.59 MiB llama_new_context_with_model: CPU compute buffer size = 299.75 MiB llama_new_context_with_model: graph nodes = 986 llama_new_context_with_model: graph splits = 1 INFO [wmain] model loaded | tid="33680" timestamp=1719707819 time=2024-06-29T17:36:59.739-07:00 level=INFO source=server.go:599 msg="llama runner started in 1.31 seconds" ``` ### OS Windows ### GPU Intel ### CPU Intel ### Ollama version 0.1.48
GiteaMirror added the bug label 2026-04-12 13:59:46 -05:00
GiteaMirror commented 2026-04-12 13:59:48 -05:00
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@olumolu commented on GitHub (Jun 30, 2024):

integrated gpu is not supported at the point of time for intel amd or other only apple m1 m2 m3 is supported

<!-- gh-comment-id:2198471264 --> @olumolu commented on GitHub (Jun 30, 2024): integrated gpu is not supported at the point of time for intel amd or other only apple m1 m2 m3 is supported
GiteaMirror commented 2026-04-12 13:59:48 -05:00
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@zhewang1-intc commented on GitHub (Jul 2, 2024):

hi, i will try to add intel igpu support(after 11gen) in a few days

<!-- gh-comment-id:2201674644 --> @zhewang1-intc commented on GitHub (Jul 2, 2024): hi, i will try to add intel igpu support(after 11gen) in a few days
GiteaMirror commented 2026-04-12 13:59:49 -05:00
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@olumolu commented on GitHub (Jul 2, 2024):

Also for amd igpus and npus please

<!-- gh-comment-id:2201970938 --> @olumolu commented on GitHub (Jul 2, 2024): Also for amd igpus and npus please
GiteaMirror commented 2026-04-12 13:59:50 -05:00
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@dhiltgen commented on GitHub (Jul 2, 2024):

Discrete Intel GPU support tracked via #1590 That may pick up some integrated GPUs, although I haven't investigated what the full support matrix will be.

<!-- gh-comment-id:2204423270 --> @dhiltgen commented on GitHub (Jul 2, 2024): Discrete Intel GPU support tracked via #1590 That may pick up some integrated GPUs, although I haven't investigated what the full support matrix will be.
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