Inference Net Schematron 3B speed on RTX 3080 and quantization-level VRAM fit.
RTX 3080 meets the minimum VRAM requirement for Q4 inference of Inference Net Schematron 3B. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 3080 can run Inference Net Schematron 3B with Q4 quantization. At approximately 126 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 8GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 2GB | 10GB | 125.69 tok/s | ✅ Fits comfortably |
| Q8 | 4GB | 10GB | 87.98 tok/s | ✅ Fits comfortably |
| FP16 | 7GB | 10GB | 47.76 tok/s | ✅ Fits comfortably |
Need a GPU with 2GB+ VRAM? These guides match your requirements.
Check current pricing links for RTX 3080 and similar cards.
Open RTX 3080 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 →Rent cloud GPUs by the hour — no upfront hardware cost.
RTX 3080 can run Inference Net Schematron 3B at Q4 with an estimated 126 tok/s.
Q4 inference is estimated to need about 2GB VRAM on this page, while RTX 3080 has 10GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.