What GPU do I need to run google/gemma-2-9b?
9.2B parameters, published in F32. View on Hugging FaceGated
Set up gemma-2-9bgemma-2-9b is published by google on Hugging Face, with 74,679 downloads and 725 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 34.4 GB | 41.3 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | V100 | 3 | $0.264/hr | ||
| FP8 (quantized) | 8.6 GB | 10.3 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 4.3 GB | 5.2 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-2-9b at its published (F32) precision: 1× RTX A6000, at $0.363/hr per GPU ($0.363/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-2-9b: common questions
Can gemma-2-9b run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 41.3 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX A6000 at $0.363/hr.
Do I need approval to download gemma-2-9b?
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 41.3 GB the model needs once you have them.
Can gemma-2-9b run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 34.4 GB, or 41.3 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 17.2 GB, or 20.7 GB with overhead. That moves it onto a 24 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM gemma-2-9b can run in?
5.2 GB, at INT4 (quantized), which fits a 6 GB card, against 41.3 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 Gemma 2 models
- gemma-2-9b-it (9.2B, BF16)
- gemma-2-9b-it (9.2B, BF16)
- gemma-2-9b-it (9.2B, BF16)
- gemma-2-9b (9.2B, BF16)
- gemma-2-27b-it (27.2B, BF16)
Related reading: RTX A6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
