What GPU do I need to run google/gemma-2-27b?
27.2B parameters, published in F32. View on Hugging FaceGated
Set up gemma-2-27bgemma-2-27b is published by google on Hugging Face, with 10,920 downloads and 211 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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 101.4 GB | 121.7 GB | V100 | 8 | $0.704/hr |
| FP8 (quantized) | 25.4 GB | 30.4 GB | RTX 4080 Super | 1 | $0.442/hr |
| cheaper alt. | RTX 4070 Super | 3 | $0.363/hr | ||
| INT4 (quantized) | 12.7 GB | 15.2 GB | RTX A5000 | 1 | $0.176/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 at its published (F32) precision: 8× V100, at $0.088/hr per GPU ($0.704/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: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Sliding-window attention: windowed layers stop growing at the window, the full-attention layers keep growing. Cached per token: 376,832 bytes at 16-bit while the context is inside the window.
| Context | KV cache, one sequence |
|---|---|
| 4K | 1.4 GB |
| 8K (model maximum) | 2.2 GB |
Computed from the layer, head and window counts in the model's own configuration (unsloth/gemma-2-27b), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
gemma-2-27b: common questions
Can gemma-2-27b run on a single GPU?
No. At FP32 it needs 121.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 16.0 GB V100, and it takes 8 of them.
Do I need approval to download gemma-2-27b?
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 121.7 GB the model needs once you have them.
Can gemma-2-27b run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 101.4 GB, or 121.7 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 50.7 GB, or 60.9 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many GPUs do I need to run gemma-2-27b?
8 at FP32. It needs 121.7 GB of VRAM and the cheapest capable live offer is a 16.0 GB V100, so 8 of them come to $0.704/hr in total.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 2 models
- gemma-2-27b-it (27.2B, BF16)
- gemma-2-27b (27.2B, BF16)
- gemma-2-9b-it (9.2B, BF16)
- gemma-2-9b (9.2B, F32)
- gemma-2-9b (9.2B, BF16)
Lineage
Quantized and fine-tuned versions
Alternatives at this size
Other models for text-generation within about a third of gemma-2-27b's 27.2B parameters, from other model lines.
- Qwen3-32B (32.8B, BF16)
- GLM-4.7-Flash (31.2B, BF16)
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B, BF16)
- gpt-neox-20b (20.7B, F16)
- granite-4.1-30b (28.9B, BF16)
More on gemma-2-27b
Fits on a 141 GB GPU at FP32: every model that fits in 141 GB.
Fits on a 32 GB GPU at FP8: every model that fits in 32 GB.
Fits on a 16 GB GPU at INT4: every model that fits in 16 GB.
Best chat and assistants models: how gemma-2-27b ranks against the rest.
Related reading: V100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
