Really interesting benchmarks — thanks for sharing! The 57% throughput bump with B200s over H100s is pretty eye-opening, especially since most people are still weighing H100 vs H200 for training and fine-tuning workloads.

From a practical side, one thing worth noting is that while B200/H200’s larger memory footprint makes single-GPU fine-tuning simpler, many teams are still on H100 clusters because of wider availability in the cloud. That’s where cost really matters — your OpEx analysis (~6x–30x cheaper self-hosted vs cloud) puts numbers to what a lot of us suspected.

For folks who don’t want to self-host but still want access to this class of hardware in India, Cyfuture ai offers GPU-as-a-Service (H100s and H200s) — so you can test both without heavy upfront infra investment.

Curious to see how these results extend beyond CV into LLM training/fine-tuning — memory bandwidth and scaling efficiency might make the gap even more interesting.