Google Gemma 2 9B It speed on NVIDIA L4 and quantization-level VRAM fit.
NVIDIA L4 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 L4 can run Google Gemma 2 9B It with Q4 quantization. At approximately 45 tokens/second, you can expect Moderate speed - useful for batch processing.
You have 19GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 5GB | 24GB | 45.43 tok/s | ✅ Fits comfortably |
| Q8 | 9GB | 24GB | 31.80 tok/s | ✅ Fits comfortably |
| FP16 | 18GB | 24GB | 17.26 tok/s | ✅ Fits comfortably |
Check current pricing links for NVIDIA L4 and similar cards.
Open NVIDIA L4 buy links →Use workload-focused recommendations before committing to a purchase.
Browse best GPU guides →Compare complete systems if you want ready-to-run hardware.
Compare prebuilt systems →NVIDIA L4 can run Google Gemma 2 9B It at Q4 with an estimated 45 tok/s.
Q4 inference is estimated to need about 5GB VRAM on this page, while NVIDIA L4 has 24GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.