Google Gemma 2 27B It speed on NVIDIA L40 and quantization-level VRAM fit.
NVIDIA L40 meets the minimum VRAM requirement for Q4 inference of Google Gemma 2 27B It. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
NVIDIA L40 can run Google Gemma 2 27B It with Q4 quantization. At approximately 91 tokens/second, you can expect Good speed - acceptable for interactive use.
You have 34GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 14GB | 48GB | 91.10 tok/s | ✅ Fits comfortably |
| Q8 | 27GB | 48GB | 63.77 tok/s | ✅ Fits comfortably |
| FP16 | 54GB | 48GB | 34.62 tok/s | ❌ Not recommended |
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NVIDIA L40 can run Google Gemma 2 27B It at Q4 with an estimated 91 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while NVIDIA L40 has 48GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.