Google Gemma 2 9B 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 9B 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 9B It with Q4 quantization. At approximately 134 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 43GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 5GB | 48GB | 133.87 tok/s | ✅ Fits comfortably |
| Q8 | 9GB | 48GB | 93.71 tok/s | ✅ Fits comfortably |
| FP16 | 18GB | 48GB | 50.87 tok/s | ✅ Fits comfortably |
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NVIDIA RTX 6000 Ada can run Google Gemma 2 9B It at Q4 with an estimated 134 tok/s.
Q4 inference is estimated to need about 5GB 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.