Google Gemma 2 27B It speed on RTX 4090 and quantization-level VRAM fit.
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 99 tokens/second, you can expect Good speed - acceptable for interactive use.
You have 10GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 14GB | 24GB | 99.00 tok/s | ✅ Fits comfortably |
| Q8 | 27GB | 24GB | 69.30 tok/s | ❌ Not recommended |
| FP16 | 54GB | 24GB | 37.62 tok/s | ❌ Not recommended |
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RTX 4090 can run Google Gemma 2 27B It at Q4 with an estimated 99 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while RTX 4090 has 24GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.