GB300 vs GH200

The GB300 FP16 dense rating of 2250 TFLOPS exceeds the GH200 FP16 dense rating of 989 TFLOPS by a factor of roughly 2.3. This margin translates into faster matrix operations during both training and inference when sparsity features remain disabled. The GB300 FP16 with sparsity rating of 4500 TFLOPS likewise exceeds the GH200 FP16 with sparsity rating of 1979 TFLOPS by the same factor. Memory bandwidth of 8000 GB/s on the GB300 supports larger batch sizes than the 4000 GB/s bandwidth of the GH200 for memory bound kernels. FP32 throughput stands at 75 TFLOPS on the GB300 and 67 TFLOPS on the GH200. These ratios remain consistent when like for like dense figures are compared against other dense figures and when like for like sparse figures are compared against other sparse figures.

The GB300 suits workloads that require the 262 GB to 288 GB memory capacity and the 8000 GB/s bandwidth for processing very large models. Its FP16 dense performance of 2250 TFLOPS accelerates dense matrix computations beyond the 989 TFLOPS delivered by the GH200. The 1400W TDP remains acceptable when maximum throughput per socket takes priority over power efficiency.

The GH200 fits deployments constrained to a 900W power envelope or existing Hopper software stacks. Its INT8 dense rating of 1979 TOPS surpasses the 187.5 TOPS of the GB300 for integer heavy inference paths. The lower TDP and established SXM form factor support continued use in power limited clusters without immediate architecture migration.