Is NVIDIA’s B200 Really Better Than H200 for AI Training and Inference? (www.hpc-ai.com)

NVIDIA’s new Blackwell-powered B200 arrived as a clear generational jump over Hopper H200 in independent tests: the GPU packs ~208 billion transistors, 180 GB HBM3e and up to 20 PFLOPS of FP4 compute. Benchmarks on HPC-AI’s 8× B200-SXM5-180GB testbed show the B200 roughly doubles raw dense-math throughput (GEMM: H200 652.4 TFLOPS vs B200 1420.4 TFLOPS) and delivers the kind of low‑precision performance that speeds up transformer-heavy workloads. In distributed settings the B200 also improved PyTorch All-Reduce throughput (8‑GPU: H200 245.4 GB/s vs B200 293.6 GB/s), though NCCL microbenchmarks were mixed, so gains depend on stack and collective implementation. Crucially, those microbench gains translate to substantial real-world LLM speedups: Colossal-AI training showed ~50% higher sample throughput for a 7B model on 8 GPUs (H200 17.1 → B200 25.8 samp/s) and >70% improvement for a 70B model on 16 GPUs (3.27 → 5.66 samp/s), with higher TFLOPS/GPU on B200. Implication: for compute-bound, low-precision LLM training and latency-sensitive inference at scale, B200 offers a strong performance-per-node upgrade and better scaling in many frameworks. However, network stack, collective primitives, memory-parallel strategies and cost should be evaluated case-by-case since communication and software behavior can moderate real gains.