What GPU do I need to run RedHatAI/gemma-4-31B-it-FP8-dynamic?
31.3B parameters, published in F8_E4M3. View on Hugging Face
Set up gemma-4-31B-it-FP8-dynamicgemma-4-31B-it-FP8-dynamic is published by RedHatAI on Hugging Face, with 285,989 downloads and 23 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 29.1 GB | 35.0 GB | RTX 4090 | 1 | $0.441/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 14.6 GB | 17.5 GB | RTX A5000 | 1 | $0.176/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 gemma-4-31B-it-FP8-dynamic at its published (F8_E4M3) precision: 1× RTX 4090, at $0.441/hr per GPU ($0.441/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-4-31B-it-FP8-dynamic: common questions
Can gemma-4-31B-it-FP8-dynamic run on a single GPU?
Yes, but not on a desktop card. At FP8 (native) it needs 35.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX 4090 at $0.441/hr.
Is gemma-4-31B-it-FP8-dynamic already quantized?
Yes. It is published in FP8, one byte per parameter, so the 35.0 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 17.5 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.
What is the least VRAM gemma-4-31B-it-FP8-dynamic can run in?
17.5 GB, at INT4 (quantized), which fits a 24 GB card, against 35.0 GB at FP8 (native). That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing gemma-4-31B-it-FP8-dynamic lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX 4090 at $0.441/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 4 models
- gemma-4-31B-it (31.3B, BF16)
- gemma-4-31B-it-FP8-block (31.3B, F8_E4M3)
- Artemis-31B-v1.2 (31.3B, BF16)
- gemma-4-31B (32.7B, BF16)
- gemma-4-31B-it-uncensored (32.7B, BF16)
Related reading: RTX 4090 pricing and specs, and The best GPUs for AI, ranked.
