What GPU do I need to run unsloth/gemma-4-E2B-it-unsloth-bnb-4bit?
5.1B parameters, published in BF16. View on Hugging Face
Set up gemma-4-E2B…unsloth-bnb-4bitgemma-4-E2B-it-unsloth-bnb-4bit is published by unsloth on Hugging Face, with 103,400 downloads and 9 likes to date. It's a Gemma4ForConditionalGeneration model built for image-text-to-text, published natively in BF16.
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 | 9.5 GB | 11.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 4.8 GB | 5.7 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 2.4 GB | 2.9 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-4-E2B-it-unsloth-bnb-4bit 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-4-E2B-it-unsloth-bnb-4bit: common questions
Does gemma-4-E2B-it-unsloth-bnb-4bit fit on a 12 GB GPU?
Yes. At BF16 it needs 11.5 GB of VRAM, so a 12 GB card holds it with 0.5 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM gemma-4-E2B-it-unsloth-bnb-4bit can run in?
2.9 GB, at INT4 (quantized), which fits a 6 GB card, against 11.5 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 4 models
- gemma-4-E2B-it (5.1B, BF16)
- gemma-4-E2B-it-qat-w4a16-ct (5.6B, BF16)
- Gemma-4-E4B-DECKARD-HERETIC-NVFP4 (6.2B, BF16)
- gemma-4-E4B-it-W4A16-AutoRound-GPTQ (4.0B, BF16)
- gemma4-e4b-claims-comparison (7.9B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
