Nousresearch Meta Llama 3.1 8B Instruct speed on RTX 5080 and quantization-level VRAM fit.
RTX 5080 meets the minimum VRAM requirement for Q4 inference of Nousresearch Meta Llama 3.1 8B Instruct. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 5080 can run Nousresearch Meta Llama 3.1 8B Instruct with Q4 quantization. At approximately 158 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 12GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 4GB | 16GB | 158.10 tok/s | ✅ Fits comfortably |
| Q8 | 8GB | 16GB | 110.67 tok/s | ✅ Fits comfortably |
| FP16 | 16GB | 16GB | 60.08 tok/s | ⚠️ Tight fit |
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 Nousresearch Meta Llama 3.1 8B Instruct at Q4 with an estimated 158 tok/s.
Q4 inference is estimated to need about 4GB 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.