Meta AI FP8 405B Parameters

VRAM Requirements for
Llama 3.1 405B FP8

To run Llama 3.1 405B locally at FP8 quantization, you need at minimum 511.2 GB of GPU VRAM.

511.2 GB
Required VRAM
404 GB
File Size
131K tokens
Context Window
405B
Parameters
Estimated VRAM Required
511.2
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 351.2 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 351.2 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (FP8) 404 GB
Context Overhead (131,072 tokens × 405B × 2 ÷ 1M) 106.168 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 511.2 GB

Try a Different Quantization

Use the interactive calculator to compare Llama 3.1 405B across all available formats.

Open Live Calculator →

Llama 3.1 405B — Other Quantizations

Q4_K_M 229 GB file
338.5 GB
FP8 Current
511.2 GB
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Frequently Asked Questions

Can I run Llama 3.1 405B FP8 on a consumer GPU?
Running Llama 3.1 405B FP8 locally requires 511.2 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Llama 3.1 405B?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is FP8 quality good enough for production?
FP8 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.