Nvidia B300 vs H200: GPU Specs and Performance Analysis
CanopyWave's analysis compares Nvidia's B300 and H200 GPUs across architecture, memory bandwidth, and compute performance. The B300 offers significant improvements over the H200 in AI training and inference workloads, including higher flop rates and faster memory. The article provides detailed benchmark data and spec comparisons to help data centers choose the optimal GPU for their needs.
Background
Nvidia's B300 "Blackwell Ultra" GPU is the successor to the H200 (Hopper architecture) for AI training and inference. Key changes: B300 moves to a "rack-scale" design where 72 GPUs (GB300) are networked as one giant virtual GPU, versus the H200's 8-GPU node. B300 doubles HBM3e memory to 288 GB per GPU and nearly doubles memory bandwidth to 12+ TB/s. It also adds a new FP4 data type for faster low-precision inference. These improvements target the largest AI clusters — B300 systems won't be single GPU purchases but massive installations (72 GPUs per rack). The shift reflects how AI workloads now demand tight co-packaging of compute, memory, and networking at the rack level rather than individual card upgrades.