✅ FITS IN Q5_K_M QUANTIZATION
Llama-3.1-405B-Instruct on 4x NVIDIA A100 80GB (320GB) (320GB VRAM)
Full FP16 (838.92 GB) exceeds 320 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 257.51 GB (252.9 GB weights + 3.94 GB KV cache at 8K context), delivering ~5.1 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
257.51 GB / 320 GB
🟦 Weights (252.9 GB)
🟪 KV Cache (3.94 GB)
🟨 Activation Buffer (0.68 GB)
Total Required VRAM257.51 GB
Available Hardware VRAM320 GB (4x NVIDIA A100 80GB (320GB))
VRAM Headroom / Overflow62.49 GB
Estimated Generation Speed~5.1 tokens/sec
Architecture & AttentionDense-GQA (126L, 8 KV heads)
🚀 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
CLOUD GPU ALTERNATIVE
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 → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Llama-3.1-405B-Instruct on 4x NVIDIA A100 80GB (320GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 4x NVIDIA A100 80GB (320GB) (320GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 834.3 GB | 3.94 GB | 838.92 GB | No (Over by 518.9GB) | ~1.2 tok/s | 3x 4x NVIDIA A100 80GB (320GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 443.22 GB | 3.94 GB | 447.84 GB | No (Over by 127.8GB) | ~1.2 tok/s | 2x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 443.22 GB | 3.94 GB | 447.84 GB | No (Over by 127.8GB) | ~1.2 tok/s | 2x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 342.06 GB | 3.94 GB | 346.68 GB | No (Over by 26.7GB) | ~1.2 tok/s | 2x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 296.7 GB | 3.94 GB | 301.32 GB | Tight (94%) | ~4.4 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 252.9 GB | 3.94 GB | 257.51 GB | Yes (80%) | ~5.1 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 221.61 GB | 3.94 GB | 226.23 GB | Yes (71%) | ~5.9 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 203.88 GB | 3.94 GB | 208.5 GB | Yes (65%) | ~6.4 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
Frequently Asked Questions (Llama-3.1-405B-Instruct + 4x NVIDIA A100 80GB (320GB))
Can 4x NVIDIA A100 80GB (320GB) (320GB) run Llama-3.1-405B-Instruct locally?
Yes — 4x NVIDIA A100 80GB (320GB) (320GB) can run Llama-3.1-405B-Instruct using GGUF Q5_K_M (5-bit). Full FP16 (838.92 GB) exceeds 320 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 257.51 GB (252.9 GB weights + 3.94 GB KV cache at 8K context), delivering ~5.1 tokens/sec.
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.
Compare Llama-3.1-405B-Instruct on Other GPUs
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