我在我的系统上部署了 Llama 3.1 70B 和 Llama 3.1 8B，它非常适合 8B 型号。当我测试 70B 时，它没有充分利用 GPU，并且需要很长时间才能响应。以下是系统详细信息：
CPU：Ryzen 7 3700x，RAM：48g ddr4 2400，SSD：NVME m.2，GPU：RTX 3060 ti，主板：B550 M：
sudo docker logs cybersage-lama
time=2024-12-05T09:04:12.081Z level=INFO source=server.go:105 msg="system memory" total="47.0 GiB" free="45.8 GiB" free_swap="3.9 GiB"
time=2024-12-05T09:04:12.082Z level=INFO source=memory.go:343 msg="offload to cuda" layers.requested=-1 layers.model=81 layers.offload=10 layers.split="" memory.available="[7.5 GiB]" memory.gpu_overhead="0 B" memory.required.full="44.0 GiB" memory.required.partial="7.2 GiB" memory.required.kv="640.0 MiB" memory.required.allocations="[7.2 GiB]" memory.weights.total="38.9 GiB" memory.weights.repeating="38.1 GiB" memory.weights.nonrepeating="822.0 MiB" memory.graph.full="324.0 MiB" memory.graph.partial="1.1 GiB"
time=2024-12-05T09:04:12.085Z level=INFO source=server.go:380 msg="starting llama server" cmd="/usr/lib/ollama/runners/cuda_v12/ollama_llama_server --model /root/.ollama/models/blobs/sha256-de20d2cf2dc430b1717a8b07a9df029d651f3895dbffec4729a3902a6fe344c9 --ctx-size 2048 --batch-size 512 --n-gpu-layers 10 --threads 8 --parallel 1 --port 44611"
time=2024-12-05T09:04:12.086Z level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2024-12-05T09:04:12.086Z level=INFO source=server.go:559 msg="waiting for llama runner to start responding"
time=2024-12-05T09:04:12.087Z level=INFO source=server.go:593 msg="waiting for server to become available" status="llm server error"
time=2024-12-05T09:04:12.150Z level=INFO source=runner.go:939 msg="starting go runner"
time=2024-12-05T09:04:12.150Z level=INFO source=runner.go:940 msg=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 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | cgo(gcc)" threads=8
time=2024-12-05T09:04:12.150Z level=INFO source=.:0 msg="Server listening on 127.0.0.1:44611"
llama_model_loader: loaded meta data with 29 key-value pairs and 724 tensors from /root/.ollama/models/blobs/sha256-de20d2cf2dc430b1717a8b07a9df029d651f3895dbffec4729a3902a6fe344c9 (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.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 70B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 70B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 80
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 8192
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 28672
llama_model_loader: - kv 13: llama.attention.head_count u32 = 64
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 15
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
time=2024-12-05T09:04:12.341Z level=INFO source=server.go:593 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - type f32: 162 tensors
llama_model_loader: - type q4_K: 441 tensors
llama_model_loader: - type q5_K: 40 tensors
llama_model_loader: - type q6_K: 81 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 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: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_layer = 80
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
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 = 28672
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_ctx_orig_yarn = 131072
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 70B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 70.55 B
llm_load_print_meta: model size = 39.59 GiB (4.82 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 70B 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_print_meta: EOM token = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token = 128008 '<|eom_id|>'
llm_load_print_meta: EOG token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
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 Ti, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.68 MiB
llm_load_tensors: offloading 10 repeating layers to GPU
llm_load_tensors: offloaded 10/81 layers to GPU
llm_load_tensors: CPU buffer size = 40543.11 MiB
llm_load_tensors: CUDA0 buffer size = 5188.75 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: CUDA_Host KV buffer size = 560.00 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 80.00 MiB
llama_new_context_with_model: KV self size = 640.00 MiB, K (f16): 320.00 MiB, V (f16): 320.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.52 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 1088.45 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 20.01 MiB
llama_new_context_with_model: graph nodes = 2566
llama_new_context_with_model: graph splits = 914
time=2024-12-05T09:04:19.620Z level=INFO source=server.go:598 msg="llama runner started in 7.53 seconds"
这是使用 70B 向模型发送请求时 nvidia-smi 的输出：
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3060 Ti On | 00000000:0A:00.0 Off | N/A |
| 30% 57C P0 74W / 225W | 6534MiB / 8192MiB | 5% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 3129822 C ...unners/cuda_v12/ollama_llama_server 6524MiB |
+-----------------------------------------------------------------------------------------+
以下是我在机器上部署 Llama 3.1 的方法：
拉取 LLaMA Docker 映像：拉取 LLaMA Docker 映像（在本例中为 ollama/ollama）：
sudo docker pull ollama/ollama
本次测试成功。
测试 GPU 访问：您可以通过运行 CUDA 基础映像来测试 GPU 访问，以确认 Docker 识别您的 GPU：
sudo docker run --rm nvidia/cuda:11.8.0-base-ubuntu22.04 nvidia-smi
运行 LLaMA 容器：使用 GPU 访问运行 LLaMA 容器，将主机端口映射到容器的端口，无需额外的环境变量：
sudo docker run -d --gpus all -p 11434:11434 --name cybersage-lama ollama/ollama
我不知道为什么它没有充分利用 GPU 并且一切都进展缓慢。
您需要约 40GB 的 VRAM 才能在 GPU 中运行 70B 非量化模型。
从日志中可以看到，81 层中有 10 层在 GPU 中。
llm_load_tensors: offloading 10 repeating layers to GPU
llm_load_tensors: offloaded 10/81 layers to GPU
其他层将在 CPU 中运行，因此速度缓慢且 GPU 使用率低。