Llama-3.1-405B-Instruct on NVIDIA H100 80GB SXM (80GB VRAM)
Even in 4-bit Q4_K_M, Llama-3.1-405B-Instruct requires 257.51 GB VRAM (4x NVIDIA H100 80GB SXM GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~1.2 tok/s).
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
257.51 GB / 80 GB🚀 Ready-to-Run Launch Command
ollama run llama3.1:405b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-405B-Instruct-GGUF -c 8192 -ngl 99 -fa
Need 257.51 GB+ VRAM to run Llama-3.1-405B-Instruct?
Recommended cloud setup for 4-bit: Multi-GPU H100 / H200 Node (or 838.92 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
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All Quantization Formats: Llama-3.1-405B-Instruct on NVIDIA H100 80GB SXM
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA H100 80GB SXM (80GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 834.3 GB | 3.94 GB | 838.92 GB | No (Over by 758.9GB) | ~1.2 tok/s | 11x NVIDIA H100 80GB SXM |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 443.22 GB | 3.94 GB | 447.84 GB | No (Over by 367.8GB) | ~1.2 tok/s | 6x NVIDIA H100 80GB SXM |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 443.22 GB | 3.94 GB | 447.84 GB | No (Over by 367.8GB) | ~1.2 tok/s | 6x NVIDIA H100 80GB SXM |
| GGUF Q6_K (6-bit) | 6.56 bpw | 342.06 GB | 3.94 GB | 346.68 GB | No (Over by 266.7GB) | ~1.2 tok/s | 5x NVIDIA H100 80GB SXM |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 296.7 GB | 3.94 GB | 301.32 GB | No (Over by 221.3GB) | ~1.2 tok/s | 4x NVIDIA H100 80GB SXM |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 252.9 GB | 3.94 GB | 257.51 GB | No (Over by 177.5GB) | ~1.2 tok/s | 4x NVIDIA H100 80GB SXM |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 221.61 GB | 3.94 GB | 226.23 GB | No (Over by 146.2GB) | ~1.2 tok/s | 3x NVIDIA H100 80GB SXM |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 203.88 GB | 3.94 GB | 208.5 GB | No (Over by 128.5GB) | ~1.4 tok/s | 3x NVIDIA H100 80GB SXM |
Frequently Asked Questions (Llama-3.1-405B-Instruct + NVIDIA H100 80GB SXM)
Can NVIDIA H100 80GB SXM (80GB) run Llama-3.1-405B-Instruct locally?
No — A single NVIDIA H100 80GB SXM (80GB) cannot fit Llama-3.1-405B-Instruct in VRAM. Even in 4-bit Q4_K_M, Llama-3.1-405B-Instruct requires 257.51 GB VRAM (4x NVIDIA H100 80GB SXM GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~1.2 tok/s).
How is KV Cache calculated for Llama-3.1-405B-Instruct?
Llama-3.1-405B-Instruct uses Dense-GQA with 126 transformer layers, 128 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 3.94 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 1.97 GB.