Llama 3.1 405B Instruct — Hardware Requirements & GPU Compatibility
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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.
12.7K downloads
602 likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 405.9B
- Release Date
- 2024-07-16
- License
- Llama 3.1 Community
Get Started
HuggingFace
How Much VRAM Does Llama 3.1 405B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 189.7 GB | — | 172.49 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 217.6 GB | — | 197.85 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 267.9 GB | — | 243.51 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 318.1 GB | — | 289.17 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 368.3 GB | — | 334.83 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 446.4 GB | — | 405.85 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 892.9 GB | — | 811.71 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llama 3.1 405B Instruct?
Q4_K_M · 267.9 GBLlama 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 single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.1 405B Instruct?
Q4_K_M · 267.9 GB3 devices with unified memory can run Llama 3.1 405B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 512GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
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)
VRAM usage by quantization
Q4_K_M267.9 GB- 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_K189.7 GBQ4_K_M ★267.9 GBQ5_K_M318.1 GBQ6_K368.3 GBQ8_0446.4 GBBF16892.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 405B Instruct on a Mac?
Llama 3.1 405B Instruct requires at least 189.7 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 3.1 405B Instruct locally?
Yes — Llama 3.1 405B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 267.9 GB of VRAM. 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)
~19 tok/s~18 tok/s~18 tok/sReal-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 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?
3 devices with unified memory can run Llama 3.1 405B Instruct at Q4_K_M (267.9 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.