Nvidia B300 vs H200: GPU Specs and Performance Analysis (canopywave.com)

NVIDIA has announced the upcoming release of the B300 GPU, set to ship in January 2026, which boasts significant advancements through its Blackwell Ultra architecture. This new GPU delivers a staggering 14 petaFLOPS of sparse FP4 compute and comes equipped with 288GB of HBM3e memory and 8 TB/s memory bandwidth. These enhancements allow the B300 to handle larger models, such as 70B-parameter models while maintaining high inference throughput, resulting in an 11-15× increase in performance compared to the H100. This makes it particularly valuable for AI enterprises looking to optimize their inference workloads. The B300's technical specifications, which include two times the memory capacity of the H200 and three times that of the H100, position it as a frontrunner for large-scale AI applications. Enterprises utilizing B300 GPUs can expect to manage inference for large models more efficiently and at reduced operational costs. However, the GPU requires a substantial 1,400W of power, necessitating advanced cooling solutions and careful power management for data centers. As organizations plan their infrastructure, the option to leverage B300 GPUs through cloud services may emerge as a strategic choice to mitigate the associated power and cooling complexities, enhancing accessibility to next-gen AI capabilities.