Google Gemma 2 27B It speed on RX 7900 GRE and quantization-level VRAM fit.
RX 7900 GRE 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 GRE can run Google Gemma 2 27B It with Q4 quantization. At approximately 50 tokens/second, you can expect Good speed - acceptable for interactive use.
VRAM usage will be very close to your GPU's limit. Consider closing other applications or using Q3 quantization for more margin.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 14GB | 16GB | 50.16 tok/s | ✅ Fits comfortably |
| Q8 | 28GB | 16GB | 35.11 tok/s | ❌ Not recommended |
| FP16 | 55GB | 16GB | 19.06 tok/s | ❌ Not recommended |
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RX 7900 GRE can run Google Gemma 2 27B It at Q4 with an estimated 50 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while RX 7900 GRE has 16GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.