Google Gemma 2 9B It speed on NVIDIA H100 PCIe 80GB and quantization-level VRAM fit.
NVIDIA H100 PCIe 80GB 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 H100 PCIe 80GB can run Google Gemma 2 9B It with Q4 quantization. At approximately 236 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 75GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 5GB | 80GB | 235.70 tok/s | ✅ Fits comfortably |
| Q8 | 9GB | 80GB | 164.99 tok/s | ✅ Fits comfortably |
| FP16 | 18GB | 80GB | 89.57 tok/s | ✅ Fits comfortably |
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NVIDIA H100 PCIe 80GB can run Google Gemma 2 9B It at Q4 with an estimated 236 tok/s.
Q4 inference is estimated to need about 5GB VRAM on this page, while NVIDIA H100 PCIe 80GB has 80GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.