RTX 4090 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.
RTX 4090 can run google/gemma-2-27b-it with Q4 quantization. At approximately 101 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 10GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 14GB | 24GB | 100.85 tok/s | ✅ Fits comfortably |
| Q8 | 28GB | 24GB | 75.83 tok/s | ❌ Not recommended |
| FP16 | 56GB | 24GB | 39.64 tok/s | ❌ Not recommended |