What GPU do I need to run meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8?
401.6B parameters, published in F8_E4M3. View on Hugging FaceGated Full specs & deploy guide
Set up Llama-4-Mav…28E-Instruct-FP8Llama-4-Maverick-17B-128E-Instruct-FP8 is published by meta-llama on Hugging Face, with 52,485 downloads and 181 likes to date. It's a Llama4ForConditionalGeneration model built for image-text-to-text, 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 374.1 GB | 448.9 GB | RTX PRO 6000 | 5 | $8.20/hr |
| INT4 (quantized) | 187.0 GB | 224.4 GB | RTX A6000 | 5 | $1.65/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-4-Maverick-17B-128E-Instruct-FP8 at its published (F8_E4M3) precision: 5× RTX PRO 6000, at $1.64/hr per GPU ($8.20/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-4-Maverick-17B-128E-Instruct-FP8: common questions
Can Llama-4-Maverick-17B-128E-Instruct-FP8 run on a single GPU?
No. At FP8 (native) it needs 448.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 96.0 GB RTX PRO 6000, and it takes 5 of them.
Do I need approval to download Llama-4-Maverick-17B-128E-Instruct-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 448.9 GB the model needs once you have them.
Is Llama-4-Maverick-17B-128E-Instruct-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 448.9 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 224.4 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-4-Maverick-17B-128E-Instruct-FP8?
5 at FP8 (native). It needs 448.9 GB of VRAM and the cheapest capable live offer is a 96.0 GB RTX PRO 6000, so 5 of them come to $8.20/hr in total.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
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