Table des matières
GPU Bench
- RTX AI Garage sur blog de nvidia
- Gigabyte Windforce OC 12GB Geforce RTX 3060, 354 €TTC neuve 2025-11
- PNY OC 16 Go Geforce RTX 5060 Ti, 450 €TTC neuve 2025-11
Benchmark d'IA pour extraction de noms :
- avec service Mistral, modèle Codestral =
00j 01h 02m 48s - RTX3060 + Intel-i7, modèle granite-4.0-h-small-Q8_0 =
02j 16h 11m 34s
Selon LeChat:
| Carte graphique | TOPS (INT8) | TOPS (FP16) | Architecture |
|---|---|---|---|
| RTX 3060 (12 Go) | ~120 TOPS | ~60 TOPS | Ampere |
| RTX 5060 Ti (16 Go) | ~759 TOPS | ~380 TOPS | Blackwell |
Bench llama.cpp
-p: Prompt processing (pp): processing a prompt in batches-n: Text generation (tg): generating a sequence of tokens-pg: Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens
- Text generation: tg128, tg256, tg512 :
-p 0 -n 128,256,512 - Prompt processing: b128, b256, b512 :
-p 1024 -n 0 -b 128,256,512
| models | test | tokens/seconds | |||
|---|---|---|---|---|---|
| i7-1360P | i7-1360P SYCL | RTX 3060 | RTX 5060 Ti | ||
| Qwen2.5-coder-7b-instruct-q5_k_m | tg128 | 5.47 | 57.65 | 73.54 | |
| size: 5.07 GiB | tg256 | … | 57.61 | 73.32 | |
| tg512 | … | 56.20 | 71.80 | ||
| b128 | … | 1825.17 | 2840.57 | ||
| b256 | … | 1924.10 | 3209.52 | ||
| b512 | … | 1959.18 | 3271.22 | ||
| Qwen2.5-coder-7b-instruct-q8_0 | tg128 | … | 41.42 | 50.33 | |
| size: 7.54 GiB | tg256 | … | 41.38 | 50.33 | |
| tg512 | … | 40.70 | 49.62 | ||
| b128 | 13.98 | 36.34 | 1952.96 | 2972.52 | |
| b256 | … | 42.28 | 2054.09 | 3460.41 | |
| b512 | … | 45.99 | 2093.21 | 3511.29 | |
| EuroLLM-9B-Instruct-Q4_0 | tg128 | … | 56.06 | 71.41 | |
| size: 4.94 GiB | tg256 | … | 55.96 | 71.15 | |
| tg512 | … | 53.87 | 69.45 | ||
| b128 | … | 1433.95 | CUDA error | ||
| b256 | … | 1535.06 | … | ||
| b512 | … | 1559.88 | … | ||
| Qwen3-14B-UD-Q5_K_XL | tg128 | … | 30.00 | 37.66 | |
| size: 9.82 GiB | tg256 | … | 29.97 | 38.17 | |
| tg512 | … | 29.25 | 37.30 | ||
| b128 | … | 903.97 | CUDA error | ||
| b256 | … | 951.71 | … | ||
| b512 | … | 963.76 | … | ||
| Qwen3-4B-UD-Q8_K_XL | tg128 | 7.37 | 56.35 | … | |
| size: 4.70 GiB | tg256 | 6.63 | 56.35 | … | |
| tg512 | 6.24 | 54.56 | … | ||
| b128 | 20.66 | 2163.17 | … | ||
| b256 | … | 2405.27 | … | ||
| b512 | … | 2495.35 | … | ||
| GemmaCoder3-12B-IQ4_NL.gguf | tg128 | … | 40.70 | … | |
| size: 6.41 GiB | tg256 | … | 40.67 | … | |
| tg512 | … | 39.54 | … | ||
| b128 | … | 1150.11 | … | ||
| b256 | … | 1218.27 | … | ||
| b512 | … | 1253.92 | … | ||
| Gemma3-Code-Reasoning-4B.Q8_0 | tg128 | … | 66.98 | … | |
| size: 3.84 GiB | tg256 | … | 66.95 | … | |
| tg512 | … | 65.75 | … | ||
| b128 | … | 2885.80 | … | ||
| b256 | … | 3266.87 | … | ||
| b512 | … | 3457.03 | … | ||
| GemmaCoder3-12B-Q5_K_M | tg128 | … | 34.10 | … | |
| size: 7.86 GiB | tg256 | … | 34.06 | … | |
| tg512 | … | 33.28 | … | ||
| b128 | … | 1045.27 | … | ||
| b256 | … | 1108.95 | … | ||
| b512 | … | 1144.97 | … | ||
| gpt-oss 20B MXFP4 MoE | tg128 | … | 92.86 | … | |
| gpt-oss-20b-mxfp4.gguf | tg256 | … | 92.69 | … | |
| size: 11.27 GiB | tg512 | … | 88.17 | … | |
| b128 | … | 1036.08 | … | ||
| b256 | … | 1452.01 | … | ||
| b512 | … | 1744.71 | … | ||
| gpt-oss 20B Q4_K - Medium | tg128 | … | 98.05 | … | |
| gpt-oss-20b-UD-Q4_K_XL.gguf | tg256 | … | 97.20 | … | |
| size: 11.04 GiB | tg512 | … | 92.43 | … | |
| b128 | … | 1034.15 | … | ||
| b256 | … | 1450.77 | … | ||
| b512 | … | 1734.35 | … | ||
- Les “CUDA error” apparaissent avec la RTX 5060 Ti et le bridge PCIe/THB4 “Wikingoo L17” et le driver nvidia 580.
- Avec le CPU, laisser le nombre de cœurs en automatique, ce sont les physiques qui seront utilisés. Si on force plus de thread, les perfs diminuent.
- le multi-threads physique est utile. Ex: en auto 7.37 t/s, avec 1 thread 3.39 t/s
Intel® Core™ i7-1360P 13th Gen
Pour comparaison …
Qwen2.5-coder-7b-instruct-q5_k_m:
./llama-bench -m ~/Data/AI_Models/Qwen2.5-coder-7b-instruct-q5_k_m.gguf -p 0 -n 128 load_backend: loaded RPC backend from /home/.../llama-b7109/libggml-rpc.so load_backend: loaded CPU backend from /home/.../llama-b7109/libggml-cpu-alderlake.so | model | size | params | backend | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: | | qwen2 7B Q5_K - Medium | 5.07 GiB | 7.62 B | CPU | 4 | tg128 | 5.47 ± 0.72 |
Gigabyte Windforce OC 12GB Geforce RTX 3060
Avec sudo nsys-ui :
| NVIDIA GeForce RTX 3060 | |
|---|---|
| Chip Name | GA104 |
| SM Count | 28 |
| L2 Cache Size | 2,25 MiB |
| Memory Bandwidth | 335,32 GiB/s |
| Memory Size | 11,63 GiB |
| Core Clock | 1,79 GHz |
| Bus Location | 0000:05:00.0 |
| GSP firmware version | 580.105.08 |
| Video accelerator tracing | Supported |
Avec llama.cpp et CUDA 12.9.
Qwen2.5-coder-7b-instruct-q5_k_m
./build/bin/llama-bench -m ~/Data/AI_Models/Qwen2.5-coder-7b-instruct-q5_k_m.gguf -p 0 -n 128,256,512 ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes | model | size | params | backend | ngl | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: | | qwen2 7B Q5_K - Medium | 5.07 GiB | 7.62 B | CUDA | 99 | tg128 | 57.65 ± 0.03 | | qwen2 7B Q5_K - Medium | 5.07 GiB | 7.62 B | CUDA | 99 | tg256 | 57.61 ± 0.03 | | qwen2 7B Q5_K - Medium | 5.07 GiB | 7.62 B | CUDA | 99 | tg512 | 56.24 ± 0.05 |
GemmaCoder3-12B-Q5_K_M
Pour lancer llama-server avec le modèle “GemmaCoder3-12B-Q5_K_M.gguf” (fichier 8.4Go) fait de 49 layers en utilisant son contexte maximale “131072” avec --ctx-size 0 au lieu du par défaut “4096” il faut décharger des layers sur le CPU, sinon c'est main: error: unable to load model. À noter que c'est pareil avec llama-cli.
| n-gpu-layers | test | tokens/s | time | % perte perf |
|---|---|---|---|---|
| (all) 49 | tg128 | 34.15 | 0m25,904s | 0.00% |
| b128 | 1041.60 | 0m13,117s | 0.00% | |
| 44 | tg128 | 15.55 | 0m48,049s | 54.47% |
| b128 | 279.26 | 0m28,613s | 73.19% | |
| 39 | tg128 | 10.74 | 1m07,555s | 68.55% |
| b128 | 150.49 | 0m46,996s | 85.55% | |
| 30 | tg128 | 6.83 | 1m42,221s | 80.01% |
| b128 | 82.91 | 1m19,729s | 92.04% | |
| full cpu | tg128 | 3.12 | 3m28,308s | 90.86% |
| b128 | 4.50 | 22m37,674s | 99.57% |
Les valeurs qui permettent de charger ce modèle :
llama-cli:- avec son context max 131072 c'est 30 layers sur GPU :
--n-gpu-layers 30, donc 80% perte perf --ctx-size 70000 --n-gpu-layers 41- et pour tous les layers sur le GPU :
--ctx-size 42000
llama-server:--ctx-size 40000 --n-gpu-layers 44--ctx-size 43500 --n-gpu-layers 43--ctx-size 52500 --n-gpu-layers 42
Avec --ctx-size 52500 --n-gpu-layers 42 :
... NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes ... print_info: n_ctx_train = 131072 print_info: n_embd = 3840 print_info: n_embd_inp = 3840 print_info: n_layer = 48 print_info: n_head = 16 print_info: n_head_kv = 8 print_info: n_rot = 256 print_info: n_swa = 1024 print_info: is_swa_any = 1 print_info: n_embd_head_k = 256 print_info: n_embd_head_v = 256 print_info: n_gqa = 2 print_info: n_embd_k_gqa = 2048 print_info: n_embd_v_gqa = 2048 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-06 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: f_attn_scale = 6.2e-02 print_info: n_ff = 15360 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: n_expert_groups = 0 print_info: n_group_used = 0 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 2 print_info: rope scaling = linear print_info: freq_base_train = 1000000.0 print_info: freq_scale_train = 0.125 print_info: n_ctx_orig_yarn = 131072 print_info: rope_finetuned = unknown print_info: model type = 12B print_info: model params = 11.77 B print_info: general.name = gemma-3-12b-it-codeforces-SFT print_info: vocab type = SPM print_info: n_vocab = 262208 print_info: n_merges = 0 ... print_info: max token length = 48 ... load_tensors: offloading 42 repeating layers to GPU load_tensors: offloaded 42/49 layers to GPU load_tensors: CPU_Mapped model buffer size = 1720.59 MiB load_tensors: CUDA0 model buffer size = 6327.03 MiB llama_context: constructing llama_context llama_context: n_seq_max = 4 llama_context: n_ctx = 52736 llama_context: n_ctx_seq = 52736 llama_context: n_batch = 2048 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = auto llama_context: kv_unified = true llama_context: freq_base = 1000000.0 llama_context: freq_scale = 0.125 llama_context: n_ctx_seq (52736) < n_ctx_train (131072) -- the full capacity of the model will not be utilized llama_context: CPU output buffer size = 4.00 MiB llama_kv_cache_iswa: creating non-SWA KV cache, size = 52736 cells llama_kv_cache: CPU KV buffer size = 412.00 MiB llama_kv_cache: CUDA0 KV buffer size = 2884.00 MiB llama_kv_cache: size = 3296.00 MiB ( 52736 cells, 8 layers, 4/1 seqs), K (f16): 1648.00 MiB, V (f16): 1648.00 MiB llama_kv_cache_iswa: creating SWA KV cache, size = 4608 cells llama_kv_cache: CPU KV buffer size = 180.00 MiB llama_kv_cache: CUDA0 KV buffer size = 1260.00 MiB llama_kv_cache: size = 1440.00 MiB ( 4608 cells, 40 layers, 4/1 seqs), K (f16): 720.00 MiB, V (f16): 720.00 MiB llama_context: Flash Attention was auto, set to enabled llama_context: CUDA0 compute buffer size = 1307.32 MiB llama_context: CUDA_Host compute buffer size = 120.02 MiB llama_context: graph nodes = 1929 llama_context: graph splits = 94 (with bs=512), 27 (with bs=1)
PNY OC 16 Go Geforce RTX 5060 Ti
Avec vrai PCIe ✅
Sur une vrai tour avec PCIe x16 et Intel(R) Core(TM) Ultra 7 270K Plus.
Environnement et compilation sensible pour llama.cpp :
| Modèle | params | Offload GPU | Prompt (t/s) | Eval (t/s) | Total (ms) | Tokens générés | Graphs reused |
|---|---|---|---|---|---|---|---|
| Devstral-Small-2-24B-Instruct-2512-UD-Q4_K_XL | 24B | 17/41 | 427.81 – 545.85 | 0.80 – 3.19 | 123,500 – 568,458 | 9,629 – 47,241 | 0 |
| Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL | 30B | 49/49 | 590.38 – 591.76 | 28.64 – 30.06 | 4,715 – 12,818 | 19,919 – 22,804 | 294 – 530 |
| Qwen3-Coder-Next-UD-Q4_K_XL | 80B | 49/49 | 29.00 – 400.09 | 18.68 – 32.44 | 25,057 – 87,659 | 719 – 43,214 | 10 – 1,024 |
| DeepSeek-R1-Distill-Qwen-32B-Q4_K_M | 32B | 24/65 | 88.97 – 428.81 | 2.14 – 2.32 | 116,052 – 189,566 | 925 – 3,397 | 228 – 419 |
| DeepSeek-R1-Distill-Qwen-14B-Q8_0 | 14B | 24/49 | 225.55 – 775.01 | 4.10 – 4.13 | 81,383 – 147,476 | 1,307 – 3,858 | 313 – 582 |
gpt-oss-20b-UD-Q4_K_XL
$ ./llama.cpp/build/bin/llama-bench -m /data/models/gpt-oss-20b-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | test | t/s | | ------------------------- | ---------: | ---------: | ------- | --: | ------: | -------------: | | gpt-oss 20B Q4_K - Medium | 11.04 GiB | 20.91 B | CUDA | -1 | tg128 | 155.79 ± 0.21 | | gpt-oss 20B Q4_K - Medium | 11.04 GiB | 20.91 B | CUDA | -1 | tg256 | 155.81 ± 0.03 | | gpt-oss 20B Q4_K - Medium | 11.04 GiB | 20.91 B | CUDA | -1 | tg512 | 155.15 ± 0.01 | build: e25a32e98 (9584) $ ./llama.cpp/build/bin/llama-bench -m /data/models/gpt-oss-20b-UD-Q4_K_XL.gguf -p 1024 -n 0 -b 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | n_batch | test | t/s | | ------------------------- | ---------: | ------: | ------- | --: | ------: | ------: | --------------: | | gpt-oss 20B Q4_K - Medium | 11.04 GiB | 20.91 B | CUDA | -1 | 128 | pp1024 | 3308.23 ± 19.28 | | gpt-oss 20B Q4_K - Medium | 11.04 GiB | 20.91 B | CUDA | -1 | 256 | pp1024 | 4792.27 ± 39.25 | | gpt-oss 20B Q4_K - Medium | 11.04 GiB | 20.91 B | CUDA | -1 | 512 | pp1024 | 6048.13 ± 32.16 | build: e25a32e98 (9584)
Qwen2.5-coder-7b-instruct-q8_0
$ ./llama.cpp/build/bin/llama-bench -m ~/models/Qwen2.5-coder-7b-instruct-q8_0.gguf -p 0 -n 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | test | t/s | | ---------------- | ---------: | ---------: | --------- | --: | ----------: | ----------------: | | qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | -1 | tg128 | 54.23 ± 0.02 | | qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | -1 | tg256 | 54.23 ± 0.00 | | qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | -1 | tg512 | 54.12 ± 0.00 | build: e25a32e98 (9584) $ ./llama.cpp/build/bin/llama-bench -m ~/models/Qwen2.5-coder-7b-instruct-q8_0.gguf -p 1024 -n 0 -b 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | n_batch | test | t/s | | ---------------- | ---------: | ---------: | --------- | --: | ------: | --------: | ---------------: | | qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | -1 | 128 | pp1024 | 3746.31 ± 4.80 | | qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | -1 | 256 | pp1024 | 4174.39 ± 0.45 | | qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | -1 | 512 | pp1024 | 4354.18 ± 5.39 | build: e25a32e98 (9584)
Qwen2.5-coder-14b-instruct-q5_k_m
$ ./llama.cpp/build/bin/llama-bench -m ~/models/Qwen2.5-coder-14b-instruct-q5_k_m.gguf -p 0 -n 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | test | t/s | | ----------------------- | ---------: | -------: | ------- | --: | -------: | --------------: | | qwen2 14B Q5_K - Medium | 9.78 GiB | 14.77 B | CUDA | -1 | tg128 | 39.54 ± 0.02 | | qwen2 14B Q5_K - Medium | 9.78 GiB | 14.77 B | CUDA | -1 | tg256 | 39.53 ± 0.01 | | qwen2 14B Q5_K - Medium | 9.78 GiB | 14.77 B | CUDA | -1 | tg512 | 39.38 ± 0.01 | build: e25a32e98 (9584) Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | n_batch | test | t/s | | ----------------------- | ---------: | -------: | ------- | --: | ------: | ------: | --------------: | | qwen2 14B Q5_K - Medium | 9.78 GiB | 14.77 B | CUDA | -1 | 128 | pp1024 | 1835.16 ± 1.69 | | qwen2 14B Q5_K - Medium | 9.78 GiB | 14.77 B | CUDA | -1 | 256 | pp1024 | 1967.12 ± 1.01 | | qwen2 14B Q5_K - Medium | 9.78 GiB | 14.77 B | CUDA | -1 | 512 | pp1024 | 1995.02 ± 0.84 | build: e25a32e98 (9584)
gemma-4-26B-A4B-it-qat-UD-Q4_K_XL
prompt eval time = 318.17 ms / 165 tokens ( 1.93 ms per token, 518.59 tokens per second)
eval time = 1338.88 ms / 86 tokens ( 15.57 ms per token, 64.23 tokens per second)
total time = 1657.05 ms / 251 tokens
graphs reused = 1916
stop processing: n_tokens = 20931, truncated = 0
prompt eval time = 3143.73 ms / 4850 tokens ( 0.65 ms per token, 1542.75 tokens per second)
eval time = 31502.45 ms / 1854 tokens ( 16.99 ms per token, 58.85 tokens per second)
total time = 34646.18 ms / 6704 tokens
graphs reused = 3762
stop processing: n_tokens = 27604, truncated = 0
Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL
RTX 5060 Ti + Ultra 7 270K.
Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | | ------------------------------ | ---------: | ---------: | ---------- | | qwen3moe 30B.A3B Q4_K - Medium | 16.45 GiB | 30.53 B | CUDA | | qwen3moe 30B.A3B Q4_K - Medium | 16.45 GiB | 30.53 B | CUDA | | qwen3moe 30B.A3B Q4_K - Medium | 16.45 GiB | 30.53 B | CUDA | build: 931eb37f8 (9848)
$ ./llama.cpp/build/bin/llama-bench -m /data/models/Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | ngl | test | t/s | | --: | --------------: | -------------------: | llama_bench: error: failed to load model '/data/models/Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf'
Comparaison des perfs en jouant sur la décharge des MoE sur le CPU.
$ llama-bench -m Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf -p 1024 -n 0 -b 128,256,512 --n-cpu-moe 6 --n-gpu-layers 99 | ngl | n_cpu_moe | n_batch | test | t/s | | --: | ---------: | ------: | --------------: | -------------------: | | 99 | 6 | 128 | pp1024 | 697.99 ± 4.45 | | 99 | 6 | 256 | pp1024 | 1174.76 ± 9.12 | | 99 | 6 | 512 | pp1024 | 1886.14 ± 9.84 | $ llama-bench -m Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf -p 1024 -n 0 -b 128,256,512 --n-gpu-layers 44 | ngl | n_batch | test | t/s | | --: | ------: | --------------: | -------------------: | | 44 | 128 | pp1024 | 730.75 ± 4.96 | | 44 | 256 | pp1024 | 1228.75 ± 7.90 | | 44 | 512 | pp1024 | 1959.76 ± 7.00 |
$ llama-bench -m Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 --n-cpu-moe 6 --n-gpu-layers 99 | ngl | n_cpu_moe | test | t/s | | --: | ---------: | --------------: | -------------------: | | 99 | 6 | tg128 | 94.79 ± 5.26 | | 99 | 6 | tg256 | 95.48 ± 1.26 | | 99 | 6 | tg512 | 95.12 ± 3.66 | $ llama-bench -m Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 --n-gpu-layers 44 | ngl | test | t/s | | --: | --------------: | -------------------: | | 44 | tg128 | 98.73 ± 0.53 | | 44 | tg256 | 97.58 ± 0.10 | | 44 | tg512 | 94.90 ± 0.09 |
Qwen3-Coder-Next-UD-Q4_K_XL (80B)
voir le chapitre dédié.
Nemotron-Cascade-2-30B-A3B
RTX 5060 Ti + Ultra 7 270K.
Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | | ------------------------------ | ---------: | ---------: | ---------- | | nemotron_h_moe 31B.A3.5B Q4_K - Medium | 23.02 GiB | 31.58 B | CUDA | build: 931eb37f8 (9848)
$ llama-bench -m Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf -p 0 -n 128,256,512 ggml_cuda_init: found 1 CUDA devices (Total VRAM: 15849 MiB): Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | backend | ngl | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: | llama_bench: error: failed to load model '/data/models/Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf'
$ llama-bench -m Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf -p 0 -n 128,256,512 --n-gpu-layers 32 | ngl | test | t/s | | --: | --------------: | -------------------: | | 32 | tg128 | 32.65 ± 0.24 | | 32 | tg256 | 32.69 ± 0.34 | | 32 | tg512 | 32.72 ± 0.11 | $ llama-bench -m Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf -p 0 -n 128,256,512 --n-cpu-moe 24 --n-gpu-layers 99 | ngl | n_cpu_moe | test | t/s | | --: | ---------: | --------------: | -------------------: | | 99 | 24 | tg128 | 54.37 ± 0.12 | | 99 | 24 | tg256 | 54.44 ± 0.02 | | 99 | 24 | tg512 | 54.08 ± 0.30 |
$ llama-bench -m Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf -p 1024 -n 0 -b 128,256,512 --n-gpu-layers 32 | ngl | n_batch | test | t/s | | --: | ------: | --------------: | -------------------: | | 32 | 128 | pp1024 | 214.26 ± 0.55 | | 32 | 256 | pp1024 | 370.70 ± 2.07 | | 32 | 512 | pp1024 | 638.52 ± 7.24 | $ llama-bench -m Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf -p 1024 -n 0 -b 128,256,512 --n-cpu-moe 24 --n-gpu-layers 99 | ngl | n_cpu_moe | n_batch | test | t/s | | --: | ---------: | ------: | --------------: | -------------------: | | 99 | 24 | 128 | pp1024 | 242.47 ± 1.27 | | 99 | 24 | 256 | pp1024 | 422.45 ± 3.50 | | 99 | 24 | 512 | pp1024 | 750.21 ± 11.20 |
INstabilité avec eGPU 😩
Reset nvidia et CUDA:
$ sudo rmmod nvidia_uvm nvidia
- Lucie-7B_OpenLLM-France.Instruct-human-data.Q8_0.gguf
- Meta-Llama-3.1-8B-Instruct-Q8_0.gguf
- CUDA0 model buffer size = 7605,33 MiB
- CUDA0 compute buffer size = 258,50 MiB
Après 2 mois de re-essais avec des configs grub et modprobe de toutes sortes avec l'aide de forums et d'assistants (Claude, ChatGpt, LeChat), une solution apparaît sur ce ticket : forcer le PCI en “Gen 3”
# Pour récupérer l'adresse PCI "0000:05:00.0" de la RTX: lspci | grep -i nvidia sudo lspci -vvv -s 0000:05:00.0 | grep -i "LnkCap\|LnkSta" LnkCap: Port #0, Speed 32GT/s, Width x8, ASPM L1, Exit Latency L1 unlimited LnkSta: Speed 8GT/s (downgraded), Width x4 (downgraded) LnkCap2: Supported Link Speeds: 2.5-32GT/s, Crosslink- Retimer+ 2Retimers+ DRS- LnkSta2: Current De-emphasis Level: -6dB, EqualizationComplete+ EqualizationPhase1+ sudo setpci -s 0000:05:00.0 CAP_EXP+0xC.W=0x0003 sudo lspci -vvv -s 0000:05:00.0 | grep -i "LnkCap\|LnkSta" LnkCap: Port #0, Speed 32GT/s, Width x8, ASPM L1, Exit Latency L1 unlimited LnkSta: Speed 2.5GT/s (downgraded), Width x4 (downgraded) LnkCap2: Supported Link Speeds: 2.5-32GT/s, Crosslink- Retimer+ 2Retimers+ DRS- LnkSta2: Current De-emphasis Level: -6dB, EqualizationComplete+ EqualizationPhase1+
Mais non, ça a bien fonctionné avec llama-bench mais pas avec Yolo: 😩
kernel: NVRM: GPU at PCI:0000:05:00: GPU-ab296f23-e6a6-a23b-b6c1-33f9b813df84 kernel: NVRM: GPU Board Serial Number: 0 kernel: NVRM: Xid (PCI:0000:05:00): 13, Graphics Exception: Class 0xffff Subchannel 0x0 Mismatch kernel: NVRM: Xid (PCI:0000:05:00): 13, Graphics Exception: ESR 0x4041b0=0x3f20ffff kernel: NVRM: Xid (PCI:0000:05:00): 13, Graphics Exception: ESR 0x404000=0x80000002 kernel: NVRM: Xid (PCI:0000:05:00): 13, pid=6871, name=python3, Graphics Exception: channel 0x00000002, Class 0000cec0, Offset 00000100, Data deaddead
Qwen3-Coder-Next 80B
Focus sur le modèle Qwen3-Coder-Next 80B avec la “RTX 5060 Ti” avec 16 Go de VRAM connectée sur une “ASUS ProArt Z890-Creator” avec un “Intel Core Ultra 7 270K Plus” et 96 Go de DDR5 (2×48).
Llama.cpp build: 082b326fc (9951)
exploration des paramètres :
–threadsnumber of CPU threads to use during generation–n-cpu-moe–override-tensor
Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes, VRAM: 15849 MiB | model | size | params | | ------------------------------ | ---------: | ---------: | | qwen3next 80B.A3B Q4_K - Medium | 46.20 GiB | 79.67 B | | qwen3next 80B.A3B Q4_K - Medium | 46.20 GiB | 79.67 B | | qwen3next 80B.A3B Q4_K - Medium | 46.20 GiB | 79.67 B | build: 931eb37f8 (9848)
--ubatch-size
Avec un grand context: 196 k
–ubatch-sizedéfinit la taille du graphe de calcul réellement exécuté sur le GPU à chaque passe. C'est lui qui dimensionne les buffers de calcul intermédiaires (mul_mat_q, attention)
$ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf --mmap 0 --direct-io 1 \ --n-cpu-moe 39 --n-gpu-layers 99 --fit-ctx 196000 \ --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 \ --threads 7,8 -p 8192,16384 -n 256,512 -b 4096,8192 -ub 1024,2048,3072
| threads | ubatch | pp8192 (t/s) | pp16384 (t/s) | tg256 (t/s) | tg512 (t/s) |
| 7 | 1024 | 873.6 | 866.7 | 37.8 | 36.4 |
| 8 | 1024 | 871.9 | 866.1 | 38.7 | 39.8 |
| 7 | 2048 | 1121.7 | 1108.8 | 40.3 | 38.9 |
| 8 | 2048 | 1125.7 | 1107.6 | 38.0 | 39.4 |
| 7 | 3072 | 1108.6 | crash | — | — |
L'allocateur VMM CUDA de ggml gère un pool réservé par device qui a tendance à grossir au fil des configurations testées dans le même process, sans forcément être libéré/réduit entre deux réglages de contexte successifs. Du coup je passe à des commandes isolées.
./llama.cpp/build/bin/llama-bench -m /data/models/Qwen3-Coder-Next-UD-Q4_K_XL.gguf --mmap 0 --direct-io 1 \ --n-cpu-moe 39 --n-gpu-layers 99 --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 \ --threads 8 --fit-ctx 196000 -p 32768 -n 256 -b 4096,8192 -ub 2048,2560 | n_batch | n_ubatch | test | t/s | | ------: | -------: | --------------: | -------------------: | | 4096 | 2048 | pp32768 | 1073.39 ± 1.15 | | 4096 | 2560 | pp32768 | 1072.82 ± 1.55 | | 8192 | 2048 | pp32768 | 1071.79 ± 1.34 | | 8192 | 2560 | pp32768 | 1081.85 ± 1.80 | Le débit en pp plafonne après ub=2048. Avec -p 65536 ça plante ... Décharger un MoE pour un micro-batch plus grand n'est pas le bon chemin ! | n_cpu_moe | n_batch | n_ubatch | test | t/s | | ---------: | ------: | -------: | --------------: | -------------------: | | 40 | 8192 | 2048 | pp131072 | 885.22 ± 0.73 |
Avec llama-batched-bench :
#!/usr/bin/bash LLAMA_DIR="$HOME/llama.cpp/build/bin" MODEL_DIR="/data/models" # https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF MODEL="Qwen3-Coder-Next-UD-Q4_K_XL.gguf" # # ne gère pas options multiples pour "-ub" et "-b" # -b 2048,4096,6144,8192 # -ub 1024,1536,2048,2560 # "$LLAMA_DIR/llama-batched-bench" -m "$MODEL_DIR/$MODEL" \ -c 196000 \ -b 2048 \ -ub 1024 \ -ngl 99 \ --n-cpu-moe 39 \ --cache-type-k q8_0 --cache-type-v q8_0 \ --threads 8 \ --temp 1.0 --top-p 0.95 --top-k 40 --min-p 0.01 --repeat-penalty 1.0 \ --flash-attn on \ -npp 512,1024,2048,4096 \ -ntg 32,64,128 \ -npl 1 \ --output-format md == Résultats: llama_batched_bench: n_kv_max = 196096, n_batch = 2048, n_ubatch = 1024, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = 99, n_threads = 8, n_threads_batch = 8 | PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s | |-------|--------|------|--------|----------|----------|----------|----------|----------|----------| | 512 | 32 | 1 | 544 | 1.752 | 292.21 | 0.835 | 38.32 | 2.587 | 210.26 | | 512 | 64 | 1 | 576 | 1.681 | 304.54 | 1.634 | 39.17 | 3.315 | 173.75 | | 512 | 128 | 1 | 640 | 1.677 | 305.36 | 3.422 | 37.41 | 5.098 | 125.53 | | 1024 | 32 | 1 | 1056 | 2.124 | 482.20 | 0.838 | 38.18 | 2.962 | 356.54 | | 1024 | 64 | 1 | 1088 | 2.139 | 478.71 | 1.669 | 38.35 | 3.808 | 285.70 | | 1024 | 128 | 1 | 1152 | 2.156 | 474.87 | 3.327 | 38.47 | 5.484 | 210.07 | | 2048 | 32 | 1 | 2080 | 4.304 | 475.82 | 0.845 | 37.88 | 5.149 | 403.97 | | 2048 | 64 | 1 | 2112 | 4.422 | 463.18 | 1.675 | 38.20 | 6.097 | 346.41 | | 2048 | 128 | 1 | 2176 | 4.419 | 463.44 | 3.347 | 38.24 | 7.766 | 280.19 |
--n-cpu-moe
Comparaison des perfs en jouant sur la décharge des MoE sur le CPU.
“Génération”:
$ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 --n-cpu-moe 35 --n-gpu-layers 99 | ngl | n_cpu_moe | test | t/s | | --: | ---------: | --------------: | -------------------: | | 99 | 35 | tg128 | 36.52 ± 0.03 | | 99 | 35 | tg256 | 36.43 ± 0.06 | | 99 | 35 | tg512 | 36.40 ± 0.03 | $ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 --n-cpu-moe 39 --n-gpu-layers 99 | ngl | n_cpu_moe | test | t/s | | --: | ---------: | --------------: | -------------------: | | 99 | 39 | tg128 | 34.49 ± 0.03 | | 99 | 39 | tg256 | 34.53 ± 0.03 | | 99 | 39 | tg512 | 34.52 ± 0.02 | build: 931eb37f8 (9848) $ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 0 -n 128,256,512 --n-gpu-layers 16 | ngl | test | t/s | | --: | --------------: | -------------------: | | 16 | tg128 | 16.02 ± 0.08 | | 16 | tg256 | 16.09 ± 0.10 | | 16 | tg512 | 15.92 ± 0.06 |
“Prompt”
$ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 1024 -n 0 -b 128,256,512 --n-cpu-moe 35 --n-gpu-layers 99 | ngl | n_cpu_moe | n_batch | test | t/s | | --: | ---------: | ------: | --------------: | -------------------: | | 99 | 35 | 128 | pp1024 | 138.94 ± 1.34 | | 99 | 35 | 256 | pp1024 | 212.98 ± 2.30 | | 99 | 35 | 512 | pp1024 | 336.39 ± 3.29 | $ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 1024 -n 0 -b 128,256,512 --n-cpu-moe 39 --n-gpu-layers 99 | ngl | n_cpu_moe | n_batch | test | t/s | | --: | ---------: | ------: | --------------: | -------------------: | | 99 | 39 | 128 | pp1024 | 130.04 ± 0.99 | | 99 | 39 | 256 | pp1024 | 199.63 ± 1.95 | | 99 | 39 | 512 | pp1024 | 315.40 ± 2.98 | $ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 1024 -n 0 -b 128,256,512 --n-gpu-layers 16 | ngl | n_batch | test | t/s | | --: | ------: | --------------: | -------------------: | | 16 | 128 | pp1024 | 124.25 ± 0.77 | | 16 | 256 | pp1024 | 195.94 ± 1.72 | | 16 | 512 | pp1024 | 314.51 ± 2.39 |
--threads
Number of CPU threads to use during generation
À noter:
- À 8 threads la courbe
nvtopdu %GPU n'est plus droite, des créneaux apparaissent. - un réglage de
–n-cpu-moemoins agressif pour llama-server qui a besoin de VRAM pour d'autres choses
–n-cpu-moe 35 :
$ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 0 -n 512,1024 --n-cpu-moe 35 --n-gpu-layers 99 --threads 1,2,4,8 | ngl | n_cpu_moe | threads | test | t/s | | --: | ---------: | ------: | --------------: | -------------------: | | 99 | 35 | 1 | tg512 | 21.25 ± 0.10 | | 99 | 35 | 1 | tg1024 | 21.44 ± 0.07 | | 99 | 35 | 2 | tg512 | 26.96 ± 0.01 | | 99 | 35 | 2 | tg1024 | 27.08 ± 0.00 | | 99 | 35 | 4 | tg512 | 37.06 ± 0.03 | | 99 | 35 | 4 | tg1024 | 36.70 ± 0.01 | | 99 | 35 | 8 | tg512 | 41.51 ± 1.44 | <-- 🚀 | 99 | 35 | 8 | tg1024 | 39.34 ± 2.49 | nvtop: 1 thread: GPU=17%, MEM=95%, CPU=100% 2 threads: GPU=22%, MEM=95%, CPU=173% 4 threads: GPU=30%, MEM=95%, CPU=280% 8 threads: GPU=36%, MEM=95%, CPU=370%
–n-cpu-moe 39 :
$ llama-bench -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -p 0 -n 512,1024 --n-cpu-moe 39 --n-gpu-layers 99 --threads 1,2,4,8 | ngl | n_cpu_moe | threads | test | t/s | | --: | ---------: | ------: | --------------: | -------------------: | | 99 | 39 | 1 | tg512 | 16.67 ± 0.04 | | 99 | 39 | 1 | tg1024 | 16.80 ± 0.04 | | 99 | 39 | 2 | tg512 | 21.54 ± 0.05 | | 99 | 39 | 2 | tg1024 | 21.46 ± 0.03 | | 99 | 39 | 4 | tg512 | 30.36 ± 0.10 | | 99 | 39 | 4 | tg1024 | 30.46 ± 0.02 | | 99 | 39 | 8 | tg512 | 37.54 ± 1.59 | | 99 | 39 | 8 | tg1024 | 37.64 ± 0.53 | nvtop: 1 thread: GPU=15%, MEM=, CPU=100% 2 threads: GPU=20%, MEM=72%, CPU=175% 4 threads: GPU=27%, MEM=72%, CPU=290% 8 threads: GPU=35%, MEM=72%, CPU=420%
--override-tensor
Je n'ai pas trouvé la bonne recette pour remplacer –n-cpu-moe par un –override-tensor plus précis pour économiser de la mémoire sans perdre trop de performance.
./llama.cpp/build/bin/llama-bench -m /data/models/Qwen3-Coder-Next-UD-Q4_K_XL.gguf \ --mmap 0 --direct-io 1 --n-gpu-layers 99 --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 --threads 8 \ --n-cpu-moe 39 --fit-ctx 196000 \ -p 16384 -n 256 -b 8192 -ub 2560 ... --n-cpu-moe 39 | n_cpu_moe | n_batch | n_ubatch | test | t/s | | ---------: | ------: | -------: | --------------: | -------------------: | | 39 | 8192 | 2560 | pp16384 | 1117.40 ± 3.07 | | 39 | 8192 | 2560 | tg256 | 40.05 ± 1.01 | | 39 | 8192 | 2560 | pp32768 | 1082.09 ± 1.67 | | 39 | 8192 | 2560 | tg256 | 38.57 ± 2.42 | | 39 | 8192 | 2560 | pp65536 | 1007.56 ± 1.14 | | 39 | 8192 | 2560 | tg256 | 38.69 ± 1.52 | ... --n-cpu-moe 39 --override-tensor 'token_embd|output_norm=CPU' | n_cpu_moe | n_batch | n_ubatch | ot | test | t/s | | ---------: | ------: | -------: | -------------------------- | -------: | --------------: | | 39 | 8192 | 2560 | token_embd|output_norm=CPU | pp16384 | 1117.62 ± 3.23 | | 39 | 8192 | 2560 | token_embd|output_norm=CPU | tg256 | 38.65 ± 0.39 | ... --n-cpu-moe 38 | n_cpu_moe | n_batch | n_ubatch | test | t/s | | ---------: | ------: | -------: | --------------: | -------------------: | | 38 | 8192 | 2560 | pp32768 | 1081.99 ± 1.69 | | 38 | 8192 | 2560 | tg256 | 41.13 ± 0.15 | | 38 | 8192 | 2560 | pp65536 | 1007.76 ± 1.09 | | 38 | 8192 | 2560 | tg256 | 34.95 ± 1.32 | | 38 | 8192 | 2560 | pp131072 | | | 38 | 8192 | 2560 | tg256 | | ... --n-cpu-moe 38 --override-tensor 'token_embd|output_norm=CPU' | n_cpu_moe | n_batch | n_ubatch | ot | test | t/s | | ---------: | ------: | -------: | -------------------------- | -------: | --------------: | | 38 | 8192 | 2560 | token_embd|output_norm=CPU | pp32768 | 1082.00 ± 1.84 | | 38 | 8192 | 2560 | token_embd|output_norm=CPU | tg256 | 40.96 ± 0.35 | ... --n-cpu-moe 38 -ot ffn_up_exps=CPU -ot ffn_gate_exps=CPU | n_cpu_moe | n_batch | n_ubatch | ot | test | t/s | | ---------: | ------: | -------: | -------------------------- | -------: | --------------: | | 38 | 8192 | 2560 | | pp32768 | | | 38 | 8192 | 2560 | | tg256 | |
On dirait que llama calcule la sélection des tensors à placer sur le GPU ou CPU :
$ llama-bench --verbose ... ... create_tensor: loading tensor blk.46.ffn_gate_inp.weight tensor blk.46.ffn_down_exps.weight (420 MiB q6_K) buffer type overridden to CUDA_Host create_tensor: loading tensor blk.46.ffn_down_exps.weight tensor blk.46.ffn_gate_exps.weight (352 MiB q5_K) buffer type overridden to CUDA_Host create_tensor: loading tensor blk.46.ffn_gate_exps.weight tensor blk.46.ffn_up_exps.weight (352 MiB q5_K) buffer type overridden to CUDA_Host create_tensor: loading tensor blk.46.ffn_up_exps.weight create_tensor: loading tensor blk.46.ffn_gate_inp_shexp.weight create_tensor: loading tensor blk.46.ffn_gate_shexp.weight create_tensor: loading tensor blk.46.ffn_up_shexp.weight create_tensor: loading tensor blk.46.ffn_down_shexp.weight create_tensor: loading tensor blk.47.attn_norm.weight create_tensor: loading tensor blk.47.post_attention_norm.weight create_tensor: loading tensor blk.47.attn_q.weight create_tensor: loading tensor blk.47.attn_k.weight create_tensor: loading tensor blk.47.attn_v.weight create_tensor: loading tensor blk.47.attn_output.weight create_tensor: loading tensor blk.47.attn_q_norm.weight create_tensor: loading tensor blk.47.attn_k_norm.weight create_tensor: loading tensor blk.47.ffn_gate_inp.weight tensor blk.47.ffn_down_exps.weight (352 MiB q5_K) buffer type overridden to CUDA_Host create_tensor: loading tensor blk.47.ffn_down_exps.weight tensor blk.47.ffn_gate_exps.weight (288 MiB q4_K) buffer type overridden to CUDA_Host create_tensor: loading tensor blk.47.ffn_gate_exps.weight tensor blk.47.ffn_up_exps.weight (288 MiB q4_K) buffer type overridden to CUDA_Host create_tensor: loading tensor blk.47.ffn_up_exps.weight create_tensor: loading tensor blk.47.ffn_gate_inp_shexp.weight create_tensor: loading tensor blk.47.ffn_gate_shexp.weight create_tensor: loading tensor blk.47.ffn_up_shexp.weight create_tensor: loading tensor blk.47.ffn_down_shexp.weight done_getting_tensors: tensor 'blk.12.ffn_gate_inp.weight' (f32) (and 112 others) cannot be used with preferred buffer type CUDA0, using CUDA_Host instead load_tensors: offloading output layer to GPU load_tensors: offloading 47 repeating layers to GPU load_tensors: offloaded 49/49 layers to GPU load_tensors: CUDA0 model buffer size = 13378.13 MiB load_tensors: CUDA_Host model buffer size = 33926.49 MiB ...
Avec Opencode
Avec Opencode sur un gros projet Php
Context ~ 36k ~ 42k
–ubatch-size 2048 –n-cpu-moe 35 –override-tensor '(token_embd|output_norm|ffn_up_exps)=CPU' :
nvtop memory = 91%
prompt eval time = 34637.21 ms / 26409 tokens ( 1.31 ms per token, 762.45 tokens per second)
eval time = 27538.78 ms / 807 tokens ( 34.12 ms per token, 29.30 tokens per second)
total time = 62175.99 ms / 27216 tokens
graphs reused = 4024
prompt eval time = 7333.79 ms / 4423 tokens ( 1.66 ms per token, 603.10 tokens per second)
eval time = 3952.75 ms / 111 tokens ( 35.61 ms per token, 28.08 tokens per second)
total time = 11286.54 ms / 4534 tokens
graphs reused = 5107
prompt eval time = 2740.89 ms / 2051 tokens ( 1.34 ms per token, 748.30 tokens per second)
eval time = 8666.17 ms / 227 tokens ( 38.18 ms per token, 26.19 tokens per second)
total time = 11407.06 ms / 2278 tokens
graphs reused = 5940
–ubatch-size 2048 –n-cpu-moe 39 :
nvtop memory = 91%
prompt eval time = 20448.15 ms / 15360 tokens ( 1.33 ms per token, 751.17 tokens per second)
eval time = 16597.85 ms / 587 tokens ( 28.28 ms per token, 35.37 tokens per second)
total time = 37046.00 ms / 15947 tokens
graphs reused = 584
prompt eval time = 8837.18 ms / 6270 tokens ( 1.41 ms per token, 709.50 tokens per second)
eval time = 1544.96 ms / 48 tokens ( 32.19 ms per token, 31.07 tokens per second)
total time = 10382.14 ms / 6318 tokens
graphs reused = 1582
prompt eval time = 14653.00 ms / 10704 tokens ( 1.37 ms per token, 730.50 tokens per second)
eval time = 22224.00 ms / 817 tokens ( 27.20 ms per token, 36.76 tokens per second)
total time = 36877.00 ms / 11521 tokens
graphs reused = 3360
–ubatch-size 2560 –n-cpu-moe 39 :
nvtop memory = 92%
prompt eval time = 19592.18 ms / 17101 tokens ( 1.15 ms per token, 872.85 tokens per second)
eval time = 1747.92 ms / 61 tokens ( 28.65 ms per token, 34.90 tokens per second)
total time = 21340.10 ms / 17162 tokens
graphs reused = 153
prompt eval time = 8109.03 ms / 7356 tokens ( 1.10 ms per token, 907.14 tokens per second)
eval time = 5224.43 ms / 154 tokens ( 33.92 ms per token, 29.48 tokens per second)
total time = 13333.45 ms / 7510 tokens
graphs reused = 1198
init sampler, took 3.03 ms, tokens: text = 38663, total = 38663
prompt eval time = 2758.26 ms / 2168 tokens ( 1.27 ms per token, 786.00 tokens per second)
eval time = 1580.45 ms / 52 tokens ( 30.39 ms per token, 32.90 tokens per second)
total time = 4338.71 ms / 2220 tokens
graphs reused = 9322
stop processing: n_tokens = 38714, truncated = 0
init sampler, took 3.42 ms, tokens: text = 44117, total = 44117
prompt eval time = 3915.89 ms / 2673 tokens ( 1.46 ms per token, 682.60 tokens per second)
eval time = 2496.57 ms / 78 tokens ( 32.01 ms per token, 31.24 tokens per second)
total time = 6412.46 ms / 2751 tokens
graphs reused = 2677
stop processing: n_tokens = 44194, truncated = 0
init sampler, took 2.71 ms, tokens: text = 35046, total = 35046
prompt eval time = 1804.42 ms / 902 tokens ( 2.00 ms per token, 499.88 tokens per second)
eval time = 24947.27 ms / 739 tokens ( 33.76 ms per token, 29.62 tokens per second)
total time = 26751.69 ms / 1641 tokens
graphs reused = 4255
stop processing: n_tokens = 35784, truncated = 0