What GPU do I need to run meta-llama/Llama-3.1-405B-FP8?
405.9B parameters, published in F8_E4M3. View on Hugging FaceGated
Set up Llama-3.1-405B-FP8Llama-3.1-405B-FP8 is published by meta-llama on Hugging Face, with 488,296 downloads and 124 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in F8_E4M3, 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, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
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-FP8 at its published (F8_E4M3) precision: 5× RTX PRO 6000 on simplepod, at $1.00/hr per GPU ($5.00/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-FP8: common questions
Can Llama-3.1-405B-FP8 run on a single GPU?
No. At FP8 (native) it needs 453.6 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 95.0 GB RTX PRO 6000, and it takes 5 of them.
Do I need approval to download Llama-3.1-405B-FP8?
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 453.6 GB the model needs once you have them.
Is Llama-3.1-405B-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 453.6 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 226.8 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many GPUs do I need to run Llama-3.1-405B-FP8?
5 at FP8 (native). It needs 453.6 GB of VRAM and the cheapest capable live offer is a 95.0 GB RTX PRO 6000 on simplepod, so 5 of them come to $5.00/hr in total.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More meta-llama models
- Llama-3.2-1B-Instruct (1.2B, BF16)
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3-8B-Instruct (8.0B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
- Llama-3.2-1B (1.2B, BF16)
- Llama-2-7b-hf (6.7B, F16)
