Google Gemma 2 27B It speed on RTX 5080 and quantization-level VRAM fit.
RTX 5080 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 5080 can run Google Gemma 2 27B It with Q4 quantization. At approximately 87 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 | 86.96 tok/s | ✅ Fits comfortably |
| Q8 | 27GB | 16GB | 60.87 tok/s | ❌ Not recommended |
| FP16 | 54GB | 16GB | 33.04 tok/s | ❌ Not recommended |
Check current pricing links for RTX 5080 and similar cards.
Open RTX 5080 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 →RTX 5080 can run Google Gemma 2 27B It at Q4 with an estimated 87 tok/s.
Q4 inference is estimated to need about 14GB VRAM on this page, while RTX 5080 has 16GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.