Google Gemma 2 27B It speed on NVIDIA RTX 6000 Ada and quantization-level VRAM fit.
NVIDIA RTX 6000 Ada 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 RTX 6000 Ada can run Google Gemma 2 27B It with Q4 quantization. At approximately 98 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 | 98.17 tok/s | ✅ Fits comfortably |
| Q8 | 27GB | 48GB | 68.72 tok/s | ✅ Fits comfortably |
| FP16 | 54GB | 48GB | 37.31 tok/s | ❌ Not recommended |
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NVIDIA RTX 6000 Ada can run Google Gemma 2 27B It at Q4 with an estimated 98 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while NVIDIA RTX 6000 Ada has 48GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.