What GPU do I need to run RedHatAI/gemma-4-12B-it-FP8-Dynamic?
13.0B parameters, published in F8_E4M3. View on Hugging Face
gemma-4-12B-it-FP8-Dynamic is published by RedHatAI on Hugging Face, with 521,299 downloads and 5 likes to date. It's a Gemma4UnifiedForConditionalGeneration model built for any-to-any, 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-4-12B-it-FP8-Dynamic at its published (F8_E4M3) precision: 1× RTX 5060 Ti on simplepod, at $0.100/hr per GPU ($0.100/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.
More RedHatAI models
- gemma-4-31B-it-FP8-block (31.3B, F8_E4M3)
- gemma-4-26B-A4B-it-FP8-dynamic (26.5B, F8_E4M3)
- Llama-3.2-1B-Instruct-FP8-dynamic (1.5B, F8_E4M3)
- Qwen3.5-9B-FP8-dynamic (9.4B, F8_E4M3)
- gemma-4-31B-it-FP8-dynamic (31.3B, F8_E4M3)
- Llama-3.2-1B-Instruct-FP8 (1.5B, F8_E4M3)
