Llama 3.1 405B Instruct: Self-Hosting & Deployment Guide
Llama 3.1 405B Instruct is Meta's largest openly released instruction-tuned text model.
What is Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct is Meta's largest openly released instruction-tuned text model, published by Meta with open weights at meta-llama/Llama-3.1-405B-Instruct. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly.
How to run Llama 3.1 405B Instruct yourself
Open weights (llama3.1), 405.9B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
Llama 3.1 405B Instruct VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 811.7 GB | Estimate | 405.9B params × 2 bytes |
| FP8 | 410.1 GB | File size | nvidia/Llama-3.1-405B-Instruct-FP8 |
| AWQ 4-bit | 219.3 GB | File size | hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy Llama 3.1 405B Instruct on your hardware
| Hardware | BF16 (full precision) | FP8 | AWQ 4-bit |
|---|---|---|---|
| NVIDIA H200141 GB | 7× | 4× | 2× |
| NVIDIA H10080 GB | — | 6× | 4× |
| NVIDIA A100 80GB80 GB | — | 6× | 4× |
| NVIDIA L40S48 GB | — | — | 6× |
GPUs needed per variant.
| Hardware | BF16 (full precision) | FP8 | AWQ 4-bit |
|---|---|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | — | Fits | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | — | Fits | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | — | — | — |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | — | Fits | Fits |
| Azure ND H100 v58× H100 · 640 GB | — | Fits | Fits |
Whole-instance GPU memory; public instance specs.
Serve it yourself
pip install -U vllm
vllm serve meta-llama/Llama-3.1-405B-Instructpip install 'sglang[all]'
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-405B-InstructCommands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsWhy run Llama 3.1 405B Instruct on LLM.API?
Unified AI Routing
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Production: Compare provider price points and keep spend visible as you scale Llama 3.1 405B Instruct.
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Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for Llama 3.1 405B Instruct alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
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