What GPU do I need to run meta-llama/Llama-3.1-405B?
405.9B parameters, published in BF16. View on Hugging FaceGated
Set up Llama-3.1-405BLlama-3.1-405B is published by meta-llama on Hugging Face, with 141,703 downloads and 987 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.
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 | AMD MI300X | 5 | $13.14/hr |
| FP8 (quantized) | 378.0 GB | 453.6 GB | RTX PRO 6000 | 5 | $7.00/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.
Cheapest way to run Llama-3.1-405B at its published (BF16) precision: 5× AMD MI300X, at $2.63/hr per GPU ($13.14/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-3.1-405B: 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 is not published for this architecture, so no figure is shown.
Llama-3.1-405B: common questions
Can Llama-3.1-405B run on a single GPU?
No. At BF16 it needs 907.2 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 192.0 GB AMD MI300X, and it takes 5 of them.
Do I need approval to download Llama-3.1-405B?
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 many GPUs do I need to run Llama-3.1-405B?
5 at BF16. It needs 907.2 GB of VRAM and the cheapest capable live offer is a 192.0 GB AMD MI300X, so 5 of them come to $13.14/hr in total.
Does quantizing Llama-3.1-405B lower the GPU bill?
Yes. At BF16 the cheapest live fit is 5 AMD MI300X cards at $13.14/hr. At INT4 (quantized) it drops to 5 RTX A6000 cards at $1.81/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- Llama-3.1-405B-Instruct (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'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
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 ranks against the rest.
Compare Llama-3.1-405B with DeepSeek-R1, DeepSeek-V3.2, DeepSeek-V3-0324, DeepSeek-V3.
Related reading: AMD MI300X 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.
