B200 SXM 180GB vs H100 SXM5 80GB for LLM inference

B200 SXM 180GB vs H100 SXM5 80GB: verdict

B200 SXM 180GB 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 — B200 SXM 180GB is the safer pick, with 180GB on the B200 SXM 180GB 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, B200 SXM 180GB pulls ahead at 7700 GB/s of memory bandwidth on the B200 SXM 180GB 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, H100 SXM5 80GB is the cheaper rental right now. 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.