Openpipe Qwen3 14B Instruct speed on NVIDIA H100 SXM5 80GB and quantization-level VRAM fit.
NVIDIA H100 SXM5 80GB meets the minimum VRAM requirement for Q4 inference of Openpipe Qwen3 14B Instruct. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
NVIDIA H100 SXM5 80GB can run Openpipe Qwen3 14B Instruct with Q4 quantization. At approximately 371 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 73GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 7GB | 80GB | 371.30 tok/s | ✅ Fits comfortably |
| Q8 | 14GB | 80GB | 259.91 tok/s | ✅ Fits comfortably |
| FP16 | 28GB | 80GB | 141.09 tok/s | ✅ Fits comfortably |
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NVIDIA H100 SXM5 80GB can run Openpipe Qwen3 14B Instruct at Q4 with an estimated 371 tok/s.
Q4 inference is estimated to need about 7GB VRAM on this page, while NVIDIA H100 SXM5 80GB has 80GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.