What GPU do I need to run google/gemma-7b-it?
8.5B parameters, published in BF16. View on Hugging FaceGated
Set up gemma-7b-itgemma-7b-it is published by google on Hugging Face, with 23,859 downloads and 1,251 likes to date. It's a GemmaForCausalLM model built for text-generation, 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 | 15.9 GB | 19.1 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 8.0 GB | 9.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 4.0 GB | 4.8 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-7b-it at its published (BF16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-7b-it: common questions
Does gemma-7b-it fit on a 24 GB GPU?
Yes. At BF16 it needs 19.1 GB of VRAM, so a 24 GB card holds it with 4.9 GB to spare. A 16 GB card is not enough for it at BF16.
Do I need approval to download gemma-7b-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 19.1 GB the model needs once you have them.
What is the least VRAM gemma-7b-it can run in?
4.8 GB, at INT4 (quantized), which fits a 6 GB card, against 19.1 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-7b-it lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At FP8 (quantized) it drops to one RTX 5060 Ti at $0.110/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 models
- gemma-7b (8.5B, BF16)
- gemma-1.1-2b-it (2.5B, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
