What GPU do I need to run google/gemma-2-2b?
2.6B parameters, published in F32. View on Hugging FaceGated
Set up gemma-2-2bgemma-2-2b is published by google on Hugging Face, with 292,921 downloads and 694 likes to date. It's a Gemma2ForCausalLM model built for text-generation, published natively in F32, 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 at its published (F32) precision: 1× V100 on simplepod, at $0.060/hr per GPU ($0.060/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: common questions
Does gemma-2-2b fit on a 12 GB GPU?
Yes. At FP32 it needs 11.7 GB of VRAM, so a 12 GB card holds it with 0.3 GB to spare. An 8 GB card is not enough for it at FP32.
Do I need approval to download gemma-2-2b?
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 11.7 GB the model needs once you have them.
Can gemma-2-2b run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 9.7 GB, or 11.7 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 4.9 GB, or 5.8 GB with overhead. That moves it onto a 6 GB card instead of a 12 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM gemma-2-2b can run in?
1.5 GB, at INT4 (quantized), which fits a 6 GB card, against 11.7 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around 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)
