What GPU do I need to run google/gemma-3-12b-it?
12.2B parameters, published in BF16. View on Hugging FaceGated Full specs & deploy guide
Set up gemma-3-12b-itgemma-3-12b-it is published by google on Hugging Face, with 908,406 downloads and 813 likes to date. It's a Gemma3ForConditionalGeneration model built for image-text-to-text, published natively in BF16, 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) |
|---|---|---|---|---|---|
| BF16 | 22.7 GB | 27.2 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 3060 | 3 | $0.330/hr | ||
| FP8 (quantized) | 11.4 GB | 13.6 GB | RTX 5060 Ti | 1 | $0.188/hr |
| INT4 (quantized) | 5.7 GB | 6.8 GB | RTX 3070 | 1 | $0.088/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-3-12b-it at its published (BF16) precision: 1× RTX 4080 Super, at $0.338/hr per GPU ($0.338/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-3-12b-it: common questions
Does gemma-3-12b-it fit on a 32 GB GPU?
Yes. At BF16 it needs 27.2 GB of VRAM, so a 32 GB card holds it with 4.8 GB to spare. A 24 GB card is not enough for it at BF16.
Do I need approval to download gemma-3-12b-it?
Yes. google 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 27.2 GB the model needs once you have them.
What is the least VRAM gemma-3-12b-it can run in?
6.8 GB, at INT4 (quantized), which fits an 8 GB card, against 27.2 GB at BF16. 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-3-12b-it lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 4080 Super at $0.338/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.088/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More google models
- gemma-4-31B-it (31.3B, BF16)
- gemma-4-26B-A4B-it (25.8B, BF16)
- gemma-4-E4B-it (8.0B, BF16)
- gemma-4-E2B-it (5.1B, BF16)
- gemma-4-12B-it (12.0B, BF16)
- gemma-3-1b-it (1000M, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
