Google Gemma 2 27B It speed on RX 7900 XTX and quantization-level VRAM fit.
RX 7900 XTX 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.
RX 7900 XTX can run Google Gemma 2 27B It with Q4 quantization. At approximately 80 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 | 80.00 tok/s | ✅ Fits comfortably |
| Q8 | 28GB | 24GB | 56.00 tok/s | ❌ Not recommended |
| FP16 | 55GB | 24GB | 30.40 tok/s | ❌ Not recommended |
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RX 7900 XTX can run Google Gemma 2 27B It at Q4 with an estimated 80 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while RX 7900 XTX has 24GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.