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How much VRAM does LLaMA 3.1 405B need?

LLaMA 3 · 405B params · updated 2026-08-25

Headline answer

At bf16, reference 8K context, batch size 1: 818.4GB of VRAM. The smallest fitting configuration is 4× AMD MI325X 256GB.

VRAM by quantization

DtypeVRAMFits on
bf16 818.4GB 4× AMD MI325X 256GB
fp8 421.5GB 2× AMD MI325X 256GB
awq 225.2GB 1× AMD MI325X 256GB
nvfp4 242.3GB 1× AMD MI325X 256GB

Context length sensitivity

Same model, same bf16 dtype, batch size 1 — VRAM at longer context windows.

ContextVRAM (bf16)
8K818.4GB
32K831.1GB
128K881.8GB

Model facts

Parameters 405B
Architecture dense
Max context 131,072

About LLaMA 3.1 405B

How much VRAM does an LLaMA 3.1 405B need? At bf16 — the reference precision, no quality loss — a reference 8K-context, batch-1 deployment needs 818.4GB, which is enough to tell you the smallest fitting configuration is 4× AMD MI325X 256GB. That number comes straight out of our GPU calculator's architecture-aware model, the same path the interactive calculator uses, not a size-based estimate. LLaMA 3.1 405B is a LLaMA 3 model built from 405B parameters, and most of that VRAM at batch size 1 is model weights rather than KV cache or runtime overhead — which is exactly why quantization moves the needle so much. Switching to fp8 cuts the weight footprint roughly in half; AWQ (weight-only int4) goes further, at some quality cost on harder tasks; and NVFP4 lands in a similar range to AWQ but with native throughput on Blackwell GPUs instead of an emulated dtype. The table above walks through all four options with the exact VRAM total and the smallest GPU configuration each one unlocks. Context length adds its own pressure on top of the weights: every additional token of context grows the KV cache, so a 128K-context deployment of this model needs meaningfully more headroom than the 8K reference case shown as the headline number — the context-sensitivity table breaks that out at 32K and 128K wherever this model's native window allows it.

Frequently asked questions

How much VRAM does LLaMA 3.1 405B need?

At bf16, reference 8K context and batch size 1, LLaMA 3.1 405B needs 818.4GB of VRAM. The smallest fitting configuration we find is 4× AMD MI325X 256GB.

How many parameters does LLaMA 3.1 405B have?

LLaMA 3.1 405B has 405B parameters (dense — every parameter is active on every token).

Does quantization reduce LLaMA 3.1 405B's VRAM requirement?

Yes — switching from bf16 to fp8 brings the total down to 421.5GB, and weight-only int4 formats (AWQ, NVFP4) go further still. See the per-dtype table above for exact numbers.

Where do these VRAM numbers come from?

They're computed by the same calculator engine behind our interactive GPU calculator, from the model's published architecture (layers, hidden size, attention heads, KV cache config) — not a generic per-parameter estimate.

Methodology: VRAM figures are computed by the same calculator engine behind our interactive GPU calculator, from LLaMA 3.1 405B's published architecture (layer count, hidden size, attention heads, KV cache configuration) — not a generic per-parameter estimate. Reference scenario: batch size 1, vLLM framework, fp16 KV cache. Page last updated 2026-08-25. These are engineering estimates, not a guarantee — always validate against your actual serving framework and workload before committing to hardware.

Size a full deployment — concurrency, framework, quantization — in the calculator →