Google Gemma 2 27B It speed on NVIDIA A4000 and quantization-level VRAM fit.
NVIDIA A4000 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 A4000 can run Google Gemma 2 27B It with Q4 quantization. At approximately 42 tokens/second, you can expect Moderate speed - useful for batch processing.
VRAM usage will be very close to your GPU's limit. Consider closing other applications or using Q3 quantization for more margin.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 14GB | 16GB | 42.28 tok/s | ✅ Fits comfortably |
| Q8 | 28GB | 16GB | 29.60 tok/s | ❌ Not recommended |
| FP16 | 55GB | 16GB | 16.07 tok/s | ❌ Not recommended |
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NVIDIA A4000 can run Google Gemma 2 27B It at Q4 with an estimated 42 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while NVIDIA A4000 has 16GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.