Llama 3.1 405B Instruct — Hardware Requirements & GPU Compatibility

ChatFunctions

Llama 3.1 405B Instruct is Meta's largest Llama 3.1 model, a 405.9-billion-parameter dense (non-mixture-of-experts) Transformer instruction-tuned for chat, tool use, and multilingual tasks across 8 languages. At release it was the first openly available model benchmarked as competitive with leading proprietary models on general knowledge, math, and reasoning. Running it at full precision requires a multi-GPU server; even quantized, it needs a substantial multi-GPU workstation rather than a single consumer card. Context length is 131,072 tokens (128K), a large jump from the 8,192-token window of the original Llama 3. It is released under the Llama 3.1 Community License, a custom license that requires organizations with more than 700 million monthly active users to obtain a separate license from Meta and imposes an acceptable-use policy on prohibited applications. It was published in July 2024, alongside the smaller 8B and 70B Llama 3.1 models.

405.9B params · denseLlama 3.1 CommunityJul 20249.2K downloads607 likes21.2K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Llama 3.1 405B Instruct runs on a 512 GB+ Mac or a datacenter GPU

Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 275.6 GB · ~2 tok/s

Macs from 256 GB (Q3_K_M · ~3 tok/s)

Score 83.5#82 of 106 open#78 of 102 that fit ≤512 GBLlama 3.1 Community

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

83.5

90% range 79.3–87.7
#82 of 106 open models
#78 of 102 that fit ≤512 GB

Benchmarks · tier chart ↓

Among 106 open modelsHow the Composite Score is built

CodingNot enough data yet
AgentsNot enough data yet
MathNot enough data yet
Science84.690% range 76–93
Reasoning86.490% range 78–95

Run Llama 3.1 405B Instruct

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on B300 (288 GB) (12 GB headroom) · Q4_K_M

Generation speed: ~19 tok/s; generation speed
Cost per 1M output tokens: $99.37; per 1M output tokens

Deployon RunPodBest valuefrom $6.94/hr

$5+ bonus for new users

Compare GPUs →

Run Llama 3.1 405B Instruct

275.6 GB VRAM at 16K context · runs on one Instinct MI350X 288 GB

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run llama3.1:405b
Official Ollama library build.
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF -c 16384 -ngl 99 --port 8080
MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF does not list Q4_K_M; llama.cpp falls back to Q4_K_M or the first file.

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1. Splits the model over 8 GPUs of one server (tensor parallel).
$vllm serve meta-llama/Llama-3.1-405B-Instruct --max-model-len 16384 --tensor-parallel-size 8
Runs the original weights, not the Q4_K_M GGUF; needs ~893 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model mlx-community/Meta-Llama-3.1-405B-Instruct-8bit
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model mlx-community/Meta-Llama-3.1-405B-Instruct-8bit

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$MaziyarPanahi/Meta-Llama-3.1-405B-Instruct-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
B300 (288 GB)CheapestCheapest12 GB free	~19 tok/s	$99.37	Deploy on RunPodfrom $6.94/hr (opens in new tab)	288 GB · 12 GB free · ~19 tok/s · $99.37 / 1M tokens

Compare GPUs →

How Much VRAM Does Llama 3.1 405B Instruct Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	Runs on
189.7 GB	206.3 GB	259.1 GB	151.2 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Runs on Instinct MI325X 256 GB
195.3 GB	211.8 GB	264.7 GB	177.1 GB	3-bit small — 3-bit small quantization	Runs on Instinct MI325X 256 GB
217.6 GB	234.2 GB	287 GB	~197.9 GB	3-bit medium — 3-bit medium quantization	Runs on Instinct MI325X 256 GB
267.9 GB	284.4 GB	337.2 GB	~243.5 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Runs on Instinct MI350X 288 GB
318.1 GB	334.6 GB	387.4 GB	~289.2 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Runs on 5× H100 SXM 80 GB
368.3 GB	384.8 GB	437.7 GB	~334.8 GB	6-bit, very good — 6-bit quantization, very good quality	Runs on 6× H100 SXM 80 GB
446.4 GB	463 GB	515.8 GB	~405.9 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Runs on 7× H100 SXM 80 GB
892.9 GB	909.4 GB	962.2 GB	~811.7 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Runs on 8× H200 SXM 141 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

275.6GBat 16K context

Weights: 266.5 GB
KV cache: 8.8 GB
Overhead: 0.3 GB

Server-class: ~275.6 GB at 16K context · one Instinct MI350X 288 GB

Or .

Which Hardware Can Run Llama 3.1 405B Instruct?

Q4_K_M · 267.9 GB

Llama 3.1 405B Instruct (Q4_K_M) requires 267.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 349+ GB is recommended. No consumer GPU has enough memory. 1 device with unified memory can run Llama 3.1 405B Instruct, including Mac Studio (M3 Ultra, 512GB).

Needs server-class hardware

Llama 3.1 405B Instruct runs on a 512 GB+ Mac or a datacenter GPU · Q4_K_M needs ~275.6 GB

Hardware that runs this model
Hardware	Quant	Memory	Speed	Context	Hardware page
Mac 256 GB · Mac Studio (M3 Ultra, 256GB)	Q3_K_M	225.3 GB	~3 tok/s	32K ctx	Mac Studio →	Q3_K_M · 225.3 GB · ~3 tok/s · 32K ctx
Mac 512 GB · Mac Studio (M3 Ultra, 512GB)	Q4_K_M	275.6 GB	~2 tok/s	32K ctx	Mac Studio →	Q4_K_M · 275.6 GB · ~2 tok/s · 32K ctx
Instinct MI350X 288 GB · one GPU	Q4_K_M	275.6 GB	~18 tok/s	32K ctx	Instinct MI350X →	Q4_K_M · 275.6 GB · ~18 tok/s · 32K ctx
4× H100 SXM 80 GB · 320 GB	Q4_K_M	275.6 GB	—	32K ctx	H100 SXM →	Q4_K_M · 275.6 GB · 32K ctx
3× H200 SXM 141 GB · 423 GB	Q4_K_M	275.6 GB	—	32K ctx	H200 SXM →	Q4_K_M · 275.6 GB · 32K ctx
2× B200 192 GB · 384 GB	Q4_K_M	275.6 GB	—	32K ctx	B200 →	Q4_K_M · 275.6 GB · 32K ctx

Also: Instinct MI350X · Instinct MI355X · B300 · Mac Studio (M3 Ultra, 512GB) · NVIDIA DGX A100 640GB · NVIDIA DGX H100

≤ 640 GB tier →

By VRAM tier · typical GPUs and Macs at Q4_K_M

Consumer GPUs (up to 32 GB) and Macs up to 192 GB: too small for this model (needs ≥ 197.4 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

How good is Llama 3.1 405B Instruct?

The llmrun Score merges 8 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the ≤512 GB tier

Llama 3.1 405B Instruct
Other models
Best Score for its VRAM
x: VRAM (GB, log scale) · y: llmrun Score

#78 of 102 that fit ≤512 GB

Each dot is a model at the best quant that fits ≤512 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.

Full ≤512 GB ranking →

Benchmarks

GPQA Diamond
Epoch AI · Jul 2024Independent
50.9%
#55/100 open
Independent
LMArena Text
LMArena · Oct 2026Arena
1335.5
#87/188 open
Arena
AIME 2024/2025
Epoch AI · Jul 2024Independent
9.7%
#60/88 open
Independent
MMLU-Pro
TIGER-LabIndependent
73.3%
#34/146 open
Independent
SimpleBench
Epoch AI · Jul 2024Independent
23.0%
#20/25 open
Independent
MATH Level 5
Epoch AI · Jul 2024Independent
49.8%
#15/39 open
Independent

All 12 Llama 3.1 405B Instruct benchmarks

In the ≤512 GB class

102 models fit ≤512 GB

Same Score, only models that fit ≤512 GB at their best quant: this model's peers by memory need.

#1
DeepSeek V4.1 Flash 1017DeepSeek
134.4
458.4 GB
#2
MiMo V2.6 Pro RLXiaomi
133.3
499.3 GB
#3
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
#4
GLM 5.3Z.ai
130.1
453.8 GB
#5
DeepSeek V4.1 FlashDeepSeek
129.4
458.4 GB
#6
Step 5 PreviewStepFun
128.0
407.5 GB

Full ≤512 GB ranking →

Alternatives to Llama 3.1 405B Instruct

Llama 3 family · 65 models →

Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.

Best alternative at this size
Beam 1.0 501B A23B
reflection
- 36 higher Score
- 28.4 GB more VRAM
Score 119.5 · 304 GB
Compare with Llama 3.1 405B Instruct Open Beam 1.0 501B A23B
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 40.6 higher Score
- 70.4 GB less VRAM
Score 124.1 · 205.2 GB
Compare with Llama 3.1 405B Instruct Open GLM 5.3 Flash
Other vendor
DeepSeek V4.1 Flash 1017
DeepSeek
- 50.9 higher Score
- 182.9 GB more VRAM
Score 134.4 · 458.4 GB
Compare with Llama 3.1 405B Instruct Open DeepSeek V4.1 Flash 1017

Similar models

Model	Score	VRAM	tok/s	Why it is similar
MiMo V2.6 Flash RLXiaomi	131.9	186.5 GB	—needs > 24 GB	similar score
GLM 5.3Z.ai	130.1	453.8 GB	—needs > 24 GB	similar score
DeepSeek V4.1 FlashDeepSeek	129.4	458.4 GB	—needs > 24 GB	similar score
Step 5 PreviewStepFun	128.0	407.5 GB	—needs > 24 GB	similar score
DeepSeek V4 Flash 0731DeepSeek	127.6	183 GB	—needs > 24 GB	similar score
GLM 5.2Z.ai	122.9	453.8 GB	—needs > 24 GB	similar score
Qwen3 235B A22B Instruct 2507Alibaba	98.5	142.9 GB	—needs > 24 GB	similar score
Qwen3.5 4BAlibaba	98.5	3.4 GB	~201	similar score
DeepSeek v3 0324DeepSeek	93.9	412.2 GB	—needs > 24 GB	similar score
Mistral Large 3 675B Instruct 2512Mistral AI	89.6	454.4 GB	—needs > 24 GB	similar score

Specifications

Publisher: Meta
Family: Llama 3
Parameters: 405.9B
Release Date: 2024-07-16
License: Llama 3.1 Community

Resources

Frequently Asked Questions

How much VRAM does Llama 3.1 405B Instruct need?

Llama 3.1 405B Instruct requires 267.9 GB of VRAM at Q4_K_M, or 892.9 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 405.9B × 4.8 bits ÷ 8 = 243.5 GB

KV Cache + Overhead ≈ 24.4 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

VRAM usage by quantization

Q4_K_M

267.9 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Llama 3.1 405B Instruct?

No — Llama 3.1 405B Instruct requires at least 189.7 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Llama 3.1 405B Instruct?

For Llama 3.1 405B Instruct, Q4_K_M (267.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (318.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 189.7 GB.

VRAM requirement by quantization

Q2_K

189.7 GB~75%

Q3_K_M

217.6 GB~83%

Q4_K_M ★

267.9 GB~89%

Q5_K_M

318.1 GB~92%

Q6_K

368.3 GB~95%

BF16

892.9 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Llama 3.1 405B Instruct on a Mac?

Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac can run Llama 3.1 405B Instruct, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 267.9 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 512GB).

Can I run Llama 3.1 405B Instruct locally?

Yes — Llama 3.1 405B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Llama 3.1 405B Instruct?

At Q4_K_M, Llama 3.1 405B Instruct can reach ~18 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B300 → 8000 ÷ 267.9 × 0.65 = ~19 tok/s

Estimated speed at Q4_K_M (267.9 GB)

NVIDIA B300

~19 tok/s

AMD Instinct MI350X

~18 tok/s

AMD Instinct MI355X

~18 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Llama 3.1 405B Instruct?

At Q4_K_M, the download is about 243.51 GB. The full-precision BF16 version is 811.71 GB. The smallest option (Q2_K) is 172.49 GB.

Which GPUs can run Llama 3.1 405B Instruct?

No single consumer GPU has enough VRAM to run Llama 3.1 405B Instruct at Q4_K_M (267.9 GB). Multi-GPU or professional hardware is required.

Which devices can run Llama 3.1 405B Instruct?

1 device with unified memory can run Llama 3.1 405B Instruct at Q4_K_M (267.9 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.

Llama 3.1 405B Instruct — Hardware Requirements & GPU Compatibility

Llama 3.1 405B Instruct runs on a 512 GB+ Mac or a datacenter GPU

llmrun Composite Score →

Run Llama 3.1 405B Instruct

Recommended

Ways to runOther ways

Run in cloud

How Much VRAM Does Llama 3.1 405B Instruct Need?

Selected Q4_K_M

Which Hardware Can Run Llama 3.1 405B Instruct?

How good is Llama 3.1 405B Instruct?

Score vs VRAM in the ≤512 GB tier

Benchmarks

In the ≤512 GB class

Alternatives to Llama 3.1 405B Instruct

Beam 1.0 501B A23B

GLM 5.3 Flash

DeepSeek V4.1 Flash 1017

Similar models

Specifications

Resources

Frequently Asked Questions