What GPU do I need to run meta-llama/Llama-3.1-405B-Instruct?
A 406B language model for chat and instruction-following. 405.9B parameters, published in BF16. View on Hugging FaceGated
Llama-3.1-405B-Instruct is published by meta-llama on Hugging Face, with 10,002 downloads and 598 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
What Llama-3.1-405B-Instruct is
Llama-3.1-405B-Instruct is a 406B-parameter language model published by Meta on Hugging Face. It is released under Llama 3.1 Community License Agreement.
License note: Meta's own commercial license, not OSI-approved open source; the repo is gated on Hugging Face, so you accept its terms there before downloading weights. Facts in this section are sourced from Llama-3.1-405B-Instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 756.0 GB | 907.2 GB | No capable live offer found | – | – |
| FP8 (quantized) | 378.0 GB | 453.6 GB | RTX PRO 6000 | 5 | $7.54/hr |
| INT4 (quantized) | 189.0 GB | 226.8 GB | RTX A6000 | 5 | $1.81/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Llama-3.1-405B-Instruct: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for Llama-3.1-405B-Instruct is not published for this architecture, so no figure is shown.
Llama-3.1-405B-Instruct: common questions
Can Llama-3.1-405B-Instruct run on a single GPU?
Not on a desktop card. At BF16 it needs 907.2 GB of VRAM, more than a single 32 GB desktop card holds. No card currently listed on the marketplace both supports BF16 and has enough VRAM for it, so how many it would take is not something this page can answer today.
Do I need approval to download Llama-3.1-405B-Instruct?
Yes. meta-llama gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 907.2 GB the model needs once you have them.
How to run Llama-3.1-405B-Instruct
Run Llama-3.1-405B-Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve meta-llama/Llama-3.1-405B-Instruct --tensor-parallel-size 1Run Llama-3.1-405B-Instruct with Ollama
Verified against Ollama's own library listing.
ollama run llama3.1:405bDeploy Llama-3.1-405B-Instruct on Aquanode
Aquanode has no one-click deploy template for Llama-3.1-405B-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (907 GB VRAM or more).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- Llama-3.1-405B (405.9B, BF16)
- Llama-3.1-405B-FP8 (405.9B, F8_E4M3)
- Llama-3.3-70B-Instruct-FP8-dynamic (70.6B, F8_E4M3)
- Meta-Llama-3.1-70B-Instruct-FP8 (70.6B, F8_E4M3)
- Llama-3.1-70B-Instruct (70.6B, BF16)
Alternatives at this size
Other models for text-generation within about a third of Llama-3.1-405B-Instruct's 405.9B parameters, from other model lines.
- MiMo-V2.5 (310.8B, F8_E4M3)
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 (560.5B, BF16)
- Ornith-1.5-397B (403.4B, BF16)
- GLM-4.5 (358.3B, BF16)
- Hy3-preview (298.8B, BF16)
More on Llama-3.1-405B-Instruct
Fits on a 288 GB GPU at INT4: every model that fits in 288 GB.
Best chat and assistants models: how Llama-3.1-405B-Instruct ranks against the rest.
Related reading: H100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
