Google Gemma 2 9B It speed on NVIDIA A100 40GB PCIe and quantization-level VRAM fit.
NVIDIA A100 40GB PCIe 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 A100 40GB PCIe can run Google Gemma 2 9B It with Q4 quantization. At approximately 170 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 35GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 5GB | 40GB | 170.43 tok/s | ✅ Fits comfortably |
| Q8 | 9GB | 40GB | 119.30 tok/s | ✅ Fits comfortably |
| FP16 | 18GB | 40GB | 64.76 tok/s | ✅ Fits comfortably |
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NVIDIA A100 40GB PCIe can run Google Gemma 2 9B It at Q4 with an estimated 170 tok/s.
Q4 inference is estimated to need about 5GB VRAM on this page, while NVIDIA A100 40GB PCIe has 40GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.