What GPU do I need to run google/gemma-3n-E2B-it?
5.4B parameters, published in BF16. View on Hugging FaceGated
Set up gemma-3n-E2B-itgemma-3n-E2B-it is published by google on Hugging Face, with 204,901 downloads and 322 likes to date. It's a Gemma3nForConditionalGeneration model built for image-text-to-text, 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 | 10.1 GB | 12.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 5.1 GB | 6.1 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 2.5 GB | 3.0 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-3n-E2B-it at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-3n-E2B-it: common questions
Does gemma-3n-E2B-it fit on a 16 GB GPU?
Yes. At BF16 it needs 12.2 GB of VRAM, so a 16 GB card holds it with 3.8 GB to spare. A 12 GB card is not enough for it at BF16.
Do I need approval to download gemma-3n-E2B-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 12.2 GB the model needs once you have them.
What is the least VRAM gemma-3n-E2B-it can run in?
3.0 GB, at INT4 (quantized), which fits a 6 GB card, against 12.2 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 3n models
- gemma-3n-E4B-it-MLX-bf16 (7.8B, BF16)
- gemma-3n-E4B-it (7.8B, BF16)
- gemma-3n-E4B-it (8.4B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
