Llama 3.1 405B — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 405B is the largest model in the Llama family with 405 billion parameters. It represents Meta's most capable open-weight model, delivering performance competitive with leading proprietary models across reasoning, coding, math, and multilingual tasks. It features a 128K token context window. Due to its massive size, running Llama 3.1 405B locally requires significant hardware, typically multiple high-end professional GPUs with a combined VRAM of 200GB or more at reduced precision. It is primarily used in quantized formats for local inference or via multi-node setups. Released under the Llama 3.1 Community License.
Llama 3.1 405B 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)
Llama 3.1 Community
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.1 405B
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.
Recommended
vllm serve meta-llama/Llama-3.1-405B --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun 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
How Much VRAM Does Llama 3.1 405B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 189.7 GB | 206.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on Instinct MI325X 256 GB | |
| 217.6 GB | 234.2 GB | 3-bit medium — 3-bit medium quantization | Runs on Instinct MI325X 256 GB | |
| 267.9 GB | 284.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on Instinct MI350X 288 GB | |
| 318.1 GB | 334.6 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 | 6-bit, very good — 6-bit quantization, very good quality | Runs on 6× H100 SXM 80 GB | |
| 446.4 GB | 463 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 7× H100 SXM 80 GB | |
| 892.9 GB | 909.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 8× H200 SXM 141 GB |
Selected Q4_K_M
4-bit medium quantization275.6GBat 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?
Q4_K_M · 267.9 GBLlama 3.1 405B (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, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
Llama 3.1 405B runs on a 512 GB+ Mac or a datacenter GPU · Q4_K_M needs ~275.6 GB
| 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
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?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 8 with a result for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent61.6%
#56/146 open
- MMLU
Epoch AI · Jul 2024Lab-reported
Lab-reported84.4%
#7/85 open
- BIG-Bench Hard
Epoch AI · Jul 2024Lab-reported
Lab-reported82.9%
#2/38 open
- HellaSwag
Epoch AI · Jul 2024Lab-reported
Lab-reported89.2%
#1/47 open
- ARC Challenge
Epoch AI · Jul 2024Lab-reported
Lab-reported95.3%
#2/56 open
- WinoGrande
Epoch AI · Jul 2024Lab-reported
Lab-reported89.2%
#1/51 open
Alternatives to Llama 3.1 405B
Llama 3 family · 65 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 397B A17BAlibaba | 112.8 | 242.6 GB | —needs > 24 GB | same size |
| MiniMax M3.1 FlashMiniMax | — | 258.6 GB | —needs > 24 GB | same size |
| Intern S2 397BInternLM | — | 242.6 GB | —needs > 24 GB | same size |
| Ornith 1.5 397Bornith-ai | — | 242.6 GB | —needs > 24 GB | same size |
| Trinity Large ThinkingArcee AI | — | 799.6 GB | —needs > 24 GB | same size |
| Nex N2.5 Pronex-agi | — | 794.2 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 405.9B
- Release Date
- 2024-07-16
- License
- Llama 3.1 Community
Frequently Asked Questions
How much VRAM does Llama 3.1 405B need?
Llama 3.1 405B 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
Can NVIDIA GeForce RTX 5090 run Llama 3.1 405B?
No — Llama 3.1 405B 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?
For Llama 3.1 405B, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.1 405B on a Mac?
Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac can run Llama 3.1 405B, 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 locally?
Yes — Llama 3.1 405B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.1 405B?
At Q4_K_M, Llama 3.1 405B 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)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3.1 405B?
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?
No single consumer GPU has enough VRAM to run Llama 3.1 405B at Q4_K_M (267.9 GB). Multi-GPU or professional hardware is required.
Which devices can run Llama 3.1 405B?
1 device with unified memory can run Llama 3.1 405B 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.