H100 NVL 94GB vs H100 SXM5 80GB for LLM inference

H100 NVL 94GB vs H100 SXM5 80GB: verdict

H100 NVL 94GB or H100 SXM5 80GB — which one should you actually deploy on? It depends on what's constraining your workload. If you're VRAM-bound — a large model, long context windows, or many concurrent users sharing one card — H100 NVL 94GB is the safer pick, with 94GB on the H100 NVL 94GB against 80GB on the H100 SXM5 80GB. If you're throughput-bound instead, where decode speed and memory bandwidth dominate how many tokens per second you can push, H100 NVL 94GB pulls ahead at 3900 GB/s of memory bandwidth on the H100 NVL 94GB versus 3350 GB/s on the H100 SXM5 80GB — the number that usually matters more than peak TFLOPS for single-stream decode latency. On cost, we don't have fresh live pricing for both cards today, so the price block above falls back to catalog estimates rather than a live head-to-head. Compute throughput matters too, especially for prefill-heavy workloads: the spec table above lists bf16 and fp8 TFLOPS for both cards side by side, along with NVLink support (which determines whether you can tensor-parallel across multiple units) and power draw. Rather than picking on vibes, check the models table above to see which of today's popular open-weight models actually fit — in the dtype and context length you plan to run — on each of these two cards before you commit to renting or buying either one.