What GPU do I need to run RedHatAI/gemma-4-31B-it-FP8-block?
31.3B parameters, published in F8_E4M3. View on Hugging Face
gemma-4-31B-it-FP8-block is published by RedHatAI on Hugging Face, with 1,707,194 downloads and 44 likes to date. It's a Gemma4ForConditionalGeneration model built for image-text-to-text, published natively in F8_E4M3.
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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run gemma-4-31B-it-FP8-block at its published (F8_E4M3) precision: 1× RTX 6000 Ada on vastai, at $0.640/hr per GPU ($0.640/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
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