Llama 3.1 405B Instruct
By Meta · United States
Updated 2026-07-13
Overview
Meta's reference open dense model at 405B parameters, with MMLU 88.6 and HumanEval 89.0. Gated on Hugging Face and over 240GB even at Q4.
When to pick this model
- Self-hosted alternative to closed frontier APIs when you have the hardware
- Reproducible research baseline for large dense models
- Long-running batch inference where weight licensing matters more than speed
- Distillation source for smaller specialist models
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 240 GB |
| Q5_K_M | 288 GB |
| Q8_0 | 435 GB |
| FP16 (no quantization) | 810 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Llama 3.1 405B Instruct is server-class even at Q4_K_M (240 GB). Stepping up to Q8_0 nearly doubles the footprint to 435 GB, and unquantized FP16 weights take 810 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Llama 3.1 405B Instruct needs roughly 320 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 0.5 tokens/sec on entry-level GPUs, on the order of 2 tokens/sec on a mid-range card, and up to 8 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Llama 3.1 405B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Llama 3.1 405B Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 240 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 240 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 240 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 240 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 240 GB at Q4_K_M |
Which GPU should you buy to run Llama 3.1 405B Instruct?
To run Llama 3.1 405B Instruct locally at Q4, you need ~240 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 88.6 |
| HumanEval | 89 |
| GSM8K | 96.8 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Llama 3.1 405B Instruct in context: its MMLU score of 88.6 ranks #2 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 89 ranks #4 of the 26 catalog models with a published HumanEval result (catalog median 81.1); its GSM8K score of 96.8 ranks #1 of the 9 catalog models with a published GSM8K result (catalog median 83.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- The reference dense open model — widely benchmarked and well-understood
- MMLU 88.6, HumanEval 89.0
- 128k context
- Mature ecosystem support across all serving frameworks
Limitations
- 240+ GB at Q4 — needs a serious multi-GPU server
- Hugging Face gated access
- Llama 3.1 Community License with MAU clause
- Largely superseded by MoE alternatives at similar quality
Typical workloads
In our catalog grid, Llama 3.1 405B Instruct is filed under Open Dense Reference, Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Llama 3.1 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense 405B · GQA
Training: 15T tokens by Meta.
Still the canonical dense open model, but MoE alternatives now deliver comparable quality at a fraction of the inference cost.
Quick start
ollama run llama3.1:405bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Llama 3.1 405B Instruct need?
At the recommended Q4_K_M quantization, Llama 3.1 405B Instruct needs about 240 GB of VRAM. Q8_0 takes 435 GB, and unquantized FP16 weights take 810 GB.
Can Llama 3.1 405B Instruct run without a GPU?
Yes — with roughly 320 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Llama 3.1 405B Instruct support?
Llama 3.1 405B Instruct supports a 125k-token context window (128,000 tokens).
Can I use Llama 3.1 405B Instruct commercially?
Llama 3.1 405B Instruct ships under the Llama 3.1 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Llama 3.1 405B Instruct on consumer hardware?
Our compatibility engine estimates on the order of 2 tokens/sec on a mid-range GPU and up to 8 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Llama 3.1 405B Instruct should I download first?
Start with Q4_K_M (240 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.