Deepseek AI Deepseek Coder V2 Instruct 0724 speed on NVIDIA H100 SXM5 80GB and quantization-level VRAM fit.
NVIDIA H100 SXM5 80GB meets the minimum VRAM requirement for Q4 inference of Deepseek AI Deepseek Coder V2 Instruct 0724. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
NVIDIA H100 SXM5 80GB can run Deepseek AI Deepseek Coder V2 Instruct 0724 with Q4 quantization. At approximately 124 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 44GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 36GB | 80GB | 123.77 tok/s | ✅ Fits comfortably |
| Q8 | 72GB | 80GB | 86.64 tok/s | ✅ Fits comfortably |
| FP16 | 144GB | 80GB | 47.03 tok/s | ❌ Not recommended |
Check current pricing links for NVIDIA H100 SXM5 80GB and similar cards.
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Browse best GPU guides →Compare complete systems if you want ready-to-run hardware.
Compare prebuilt systems →NVIDIA H100 SXM5 80GB can run Deepseek AI Deepseek Coder V2 Instruct 0724 at Q4 with an estimated 124 tok/s.
Q4 inference is estimated to need about 36GB 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.