What GPU do I need to run unsloth/gemma-3n-E4B-it?
8.4B parameters, published in BF16. View on Hugging Face
gemma-3n-E4B-it is published by unsloth on Hugging Face, with 118,236 downloads and 10 likes to date. It's a Gemma3nForConditionalGeneration 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.
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 caveat: 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-E4B-it at its published (BF16) precision: 1× RTX 3090 on simplepod, at $0.160/hr per GPU ($0.160/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More unsloth models
- Qwen3.6-27B-NVFP4 (21.2B, F8_E4M3)
- Llama-3.2-1B-Instruct (1.2B, BF16)
- gemma-4-E4B-it-unsloth-bnb-4bit (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- GLM-4.7-Flash (31.2B, BF16)
- Qwen3-Embedding-4B (4.0B, BF16)
