What GPU do I need to run google/gemma-4-12B-it?
A 12.0B vision-language model that reads images alongside text. 12.0B parameters, published in BF16. View on Hugging Face
gemma-4-12B-it is published by google on Hugging Face, with 3,320,663 downloads and 1,515 likes to date. It's a Gemma4UnifiedForConditionalGeneration model built for any-to-any, published natively in BF16.
What gemma-4-12B-it is
gemma-4-12B-it is a 12.0B-parameter vision-language model published by Google on Hugging Face. It is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from gemma-4-12B-it's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Image understanding and captioning
- Visual question answering
- Document/OCR-style reading
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 | 22.3 GB | 26.7 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/hr | ||
| FP8 (quantized) | 11.1 GB | 13.4 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 5.6 GB | 6.7 GB | RTX 5060 Ti | 1 | $0.110/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-4-12B-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-4-12B-it: common questions
Does gemma-4-12B-it fit on a 32 GB GPU?
Yes. At BF16 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 BF16.
What is the least VRAM gemma-4-12B-it can run in?
6.7 GB, at INT4 (quantized), which fits an 8 GB card, against 26.7 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-4-12B-it lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 4080 Super at $0.338/hr. At FP8 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
How to run gemma-4-12B-it
Run gemma-4-12B-it with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve google/gemma-4-12B-it --tensor-parallel-size 1Deploy gemma-4-12B-it on Aquanode
Aquanode has no one-click deploy template for gemma-4-12B-it; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 4080 Super or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 4 models
- gemma-4-12B-it-qat-q4_0-unquantized (12.0B, BF16)
- gemma-4-12B (12.0B, BF16)
- gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 (12.0B, F16)
- Gemma-4-12B-OBLITERATED (12.0B, BF16)
- humanizer (12.0B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
