What GPU do I need to run google/gemma-2-9b-it?
9.2B parameters, published in BF16. View on Hugging FaceGated
Set up gemma-2-9b-itgemma-2-9b-it is published by google on Hugging Face, with 693,862 downloads and 915 likes to date. It's a Gemma2ForCausalLM 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.
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-2-9b-it at its published (BF16) precision: 1× RTX 3090 on akash, at $0.147/hr per GPU ($0.147/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-2-9b-it: common questions
Does gemma-2-9b-it fit on a 24 GB GPU?
Yes. At BF16 it needs 20.7 GB of VRAM, so a 24 GB card holds it with 3.3 GB to spare. A 16 GB card is not enough for it at BF16.
Do I need approval to download gemma-2-9b-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 20.7 GB the model needs once you have them.
What is the least VRAM gemma-2-9b-it can run in?
5.2 GB, at INT4 (quantized), which fits a 6 GB card, against 20.7 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-2-9b-it lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 3090 on akash at $0.147/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 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)
