What GPU do I need to run google/gemma-2-27b-it?
27.2B parameters, published in BF16. View on Hugging FaceGated
Set up gemma-2-27b-itgemma-2-27b-it is published by google on Hugging Face, with 42,384 downloads and 573 likes to date. It's a Gemma2ForCausalLM model built for text-generation, 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 | 50.7 GB | 60.9 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX 5060 Ti | 4 | $0.440/hr | ||
| FP8 (quantized) | 25.4 GB | 30.4 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/hr | ||
| INT4 (quantized) | 12.7 GB | 15.2 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-2-27b-it at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-2-27b-it: common questions
Can gemma-2-27b-it run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 60.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
Do I need approval to download gemma-2-27b-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 60.9 GB the model needs once you have them.
What is the least VRAM gemma-2-27b-it can run in?
15.2 GB, at INT4 (quantized), which fits a 16 GB card, against 60.9 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.
Does quantizing gemma-2-27b-it lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 2 models
- gemma-2-27b (27.2B, BF16)
- gemma-2-27b (27.2B, F32)
- gemma-2-9b-it (9.2B, BF16)
- gemma-2-9b (9.2B, F32)
- gemma-2-9b-it (9.2B, BF16)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
