What GPU do I need to run google/gemma-1.1-2b-it?
2.5B parameters, published in BF16. View on Hugging FaceGated
Set up gemma-1.1-2b-itgemma-1.1-2b-it is published by google on Hugging Face, with 115,651 downloads and 174 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.
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-1.1-2b-it at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-1.1-2b-it: common questions
How much VRAM does gemma-1.1-2b-it need?
5.6 GB at BF16, 2.8 GB at FP8 (quantized), 1.4 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 4.7 GB of weights plus inference overhead is the whole requirement.
Do I need approval to download gemma-1.1-2b-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 5.6 GB the model needs once you have them.
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)
