What GPU do I need to run google/gemma-2-9b?
9.2B parameters, published in F32. View on Hugging FaceGated
gemma-2-9b is published by google on Hugging Face, with 76,858 downloads and 722 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 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-2-9b at its published (F32) precision: 1× A40 on runpod, at $0.440/hr per GPU ($0.440/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 google models
- gemma-4-26B-A4B-it (25.8B, BF16)
- gemma-4-31B-it (31.3B, BF16)
- gemma-4-E4B-it (8.0B, BF16)
- gemma-3-1b-it (1000M, BF16)
- gemma-4-E2B-it (5.1B, BF16)
- gemma-4-12B-it (12.0B, BF16)
