❌ 超出单卡显存上限 (NEEDS 4x Apple M4 Pro Mac Mini (64GB))
Llama-3.1-405B-Instruct on Apple M4 Pro Mac Mini (64GB) (64GB VRAM)
You need 4x Apple M4 Pro Mac Mini (64GB) (256 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ 交互式显存、上下文长度与 CPU 卸载调优台
📊 实时显存占用与推理吞吐遥测
233.02 GB / 64 GB
🟦 模型权重 (含词表层): 228.57 GB
🟪 KV Cache 缓存: 3.94 GB
🟨 引擎与算子工作区: 0.52 GB
所需显卡显存 (GPU VRAM)233.02 GB
所需系统内存 (CPU 卸载)0.00 GB
当前硬件可用显存64 GB (1x Apple M4 Pro Mac Mini (64GB))
显存余量 / 溢出缺口-169.02 GB
预计生成速度 (Decode Speed)~1.2 tokens/sec
预计预填充速度 (Prefill Speed)~45 tokens/sec
4K 提示词首字延迟 (TTFT)~91022 ms
Transformer 层数与注意力头Dense-GQA (126L, 8/128 KV/Q heads)
🚀 一键复制启动命令 (vLLM / llama.cpp / Ollama)
ollama run llama3.1:405b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-405B-Instruct-GGUF -c 8192 -ngl 99 -fa
云显卡实时比价 & 租卡 vs 买卡回本计算器
本地显存不够？60 秒一键租用预装 vLLM/Ollama 的云端显卡 (Llama-3.1-405B-Instruct: 233.02 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 759.28 GB
租卡对比买卡回本周期： ~32 months (vs $2,199 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | 在 RunPod 一键部署 → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | 租用低价显卡 → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | 领取 $300 免费算力 → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | 领取 $200 试用金 → |
全量化精度档位显存与速度明细表: Llama-3.1-405B-Instruct × Apple M4 Pro Mac Mini (64GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x Apple M4 Pro Mac Mini (64GB) (64GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 754.37 GB | 3.94 GB | 758.82 GB | 显存不足 (+694.8GB) | ~1.2 / 45 tok/s | 12x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 389.1 GB | 3.94 GB | 393.55 GB | 显存不足 (+329.6GB) | ~1.2 / 45 tok/s | 7x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 400.76 GB | 3.94 GB | 405.21 GB | 显存不足 (+341.2GB) | ~1.2 / 45 tok/s | 7x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 309.29 GB | 3.94 GB | 313.74 GB | 显存不足 (+249.7GB) | ~1.2 / 45 tok/s | 5x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 268.7 GB | 3.94 GB | 273.15 GB | 显存不足 (+209.1GB) | ~1.2 / 45 tok/s | 5x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 228.57 GB | 3.94 GB | 233.02 GB | 显存不足 (+169.0GB) | ~1.2 / 45 tok/s | 4x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 200.99 GB | 3.94 GB | 205.44 GB | 显存不足 (+141.4GB) | ~1.2 / 45 tok/s | 4x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 184.88 GB | 3.94 GB | 189.34 GB | 显存不足 (+125.3GB) | ~1.2 / 45 tok/s | 3x |
在其他显卡上测试该模型
Llama-3.1-405B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.1-405B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.1-405B-Instruct on NVIDIA RTX A6000 (48GB)
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