What GPU do I need to run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2?
12.0B parameters, published in F16. View on Hugging Face
Set up gemma-4-12B…2.5-v2-3.5x-tau2gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 is published by yuxinlu1 on Hugging Face, with 71,786 downloads and 97 likes to date. It's a Gemma4UnifiedForConditionalGeneration model built for text-generation, published natively in F16.
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 gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 at its published (F16) precision: 1× V100 on simplepod, at $0.170/hr per GPU ($0.170/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2: common questions
Does gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 fit on a 32 GB GPU?
Yes. At FP16 it needs 26.7 GB of VRAM, so a 32 GB card holds it with 5.3 GB to spare. A 24 GB card is not enough for it at FP16.
What is the least VRAM gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 can run in?
6.7 GB, at INT4 (quantized), which fits an 8 GB card, against 26.7 GB at FP16. 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-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 lower the GPU bill?
Yes. At FP16 the cheapest live fit is one V100 on simplepod at $0.170/hr. At INT4 (quantized) it drops to one RTX 3070 on simplepod at $0.050/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More yuxinlu1 models
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