Redhatai Llama 3.3 70B Instruct FP8 Dynamic speed on NVIDIA H100 SXM5 80GB and quantization-level VRAM fit.
NVIDIA H100 SXM5 80GB meets the minimum VRAM requirement for Q4 inference of Redhatai Llama 3.3 70B Instruct FP8 Dynamic. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
NVIDIA H100 SXM5 80GB can run Redhatai Llama 3.3 70B Instruct FP8 Dynamic with Q4 quantization. At approximately 173 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 45GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 35GB | 80GB | 173.27 tok/s | ✅ Fits comfortably |
| Q8 | 70GB | 80GB | 121.29 tok/s | ✅ Fits comfortably |
| FP16 | 140GB | 80GB | 65.84 tok/s | ❌ Not recommended |
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NVIDIA H100 SXM5 80GB can run Redhatai Llama 3.3 70B Instruct FP8 Dynamic at Q4 with an estimated 173 tok/s.
Q4 inference is estimated to need about 35GB 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.