How much VRAM does Llama 3.1 405B Instruct need?

About 258.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 273.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Llama 3.1 405B Instruct run on a 512GB GPU?

Yes — at Q4_K_M and 4K context it needs about 258.7 GB, which leaves 125.3 GB spare on a Mac M5 Ultra 512G. That is the smallest card in this index that clears it comfortably; 1 of 78 do.

Which quantization of Llama 3.1 405B Instruct should I use?

Q4_K_M has the lowest published quality loss (2.3%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q3_K_M, AWQ INT4.

Llama 3.1 405B Instruct

405B

Meta Llama 3.1

Meta frontier dense 405B. Q4 needs ~230GB+ VRAM; dual H100 80G or 8× consumer GPU.

Pro GPU

131K

Max Context

Quant Variants

GGUF Q4_K_M

Best Quality

+2.3%

Perplexity rise (best build)

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	258.7 GB	2.3%	—	Estimated	Calc HF
GGUF	Q3_K_M	3.87	206.9 GB	5.0%	—	Estimated	Calc HF
AWQ	INT4	4	213.8 GB	3.5%	—	Estimated	Calc HF

Which file each size is for

A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.

GGUF Q4_K_MSized at the calculator's generic rate for this level; no file was checked.
GGUF Q3_K_MSized at the calculator's generic rate for this level; no file was checked.
AWQ INT4Sized at the calculator's generic rate for this level; no file was checked.

Similar models

Compare with Llama 3.1

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Llama 3.1 8B Instruct

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Consumer GPUMac / Apple Silicon

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Meta's flagship 8B model with 128K context. Best-in-class for local deployment.

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70B

Llama 3.3 70B Instruct

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Running Llama 3.1 405B Instruct locally

At Q4_K_M and 4K of context, Llama 3.1 405B Instruct needs about 258.7 GB — 233.2 GB of weights, 1.97 GB of KV cache and a 23.5 GB activation buffer. The smallest card in this index that clears that comfortably is the Mac M5 Ultra 512G at 512 GB, and 1 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 13.78 GB, taking the total to 273.9 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 326 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q4_K_M carries the lowest published perplexity loss at 2.3%. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Llama 3.1 405B Instruct need?: About 258.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 273.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Llama 3.1 405B Instruct run on a 512GB GPU?: Yes — at Q4_K_M and 4K context it needs about 258.7 GB, which leaves 125.3 GB spare on a Mac M5 Ultra 512G. That is the smallest card in this index that clears it comfortably; 1 of 78 do.
Which quantization of Llama 3.1 405B Instruct should I use?: Q4_K_M has the lowest published quality loss (2.3%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q3_K_M, AWQ INT4.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: Mac M5 Ultra 512G512GB · +125.3
Just misses: Mac M5 Ultra 256G+66.7GB over Mac M2 Ultra 192G+114.7GB over
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-11-11 RSS → /feed.xml