What GPU do I need to run google/gemma-2-2b-it?
2.6B parameters, published in BF16. View on Hugging FaceGated
Set up gemma-2-2b-itgemma-2-2b-it is published by google on Hugging Face, with 764,579 downloads and 1,474 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-2-2b-it at its published (BF16) precision: 1× RTX 3060 on simplepod, at $0.080/hr per GPU ($0.080/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-2-2b-it: common questions
How much VRAM does gemma-2-2b-it need?
5.8 GB at BF16, 2.9 GB at FP8 (quantized), 1.5 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 4.9 GB of weights plus inference overhead is the whole requirement.
Do I need approval to download gemma-2-2b-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 5.8 GB the model needs once you have them.
How many copies of gemma-2-2b-it fit on one RTX 3060?
2, by VRAM alone. That card carries 12.0 GB and one copy needs 5.8 GB at BF16, on a live rate of $0.080/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
Does quantizing gemma-2-2b-it lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 3060 on simplepod at $0.080/hr. At INT4 (quantized) it drops to one A16 on vultr at $0.059/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)
