GB300 vs H100

The FP16 dense rating of 2250 TFLOPS on the GB300 exceeds the 989 TFLOPS dense FP16 on the H100 by more than double. This gap allows the GB300 to handle larger model training sessions or faster inference passes in dense precision modes. The FP32 figures of 75 TFLOPS versus 67 TFLOPS indicate a modest advantage for the GB300 in general purpose computations. Memory bandwidth of 8000 GB/s on the GB300 compared to 3350 GB/s on the H100 supports substantially larger batch sizes during both training and inference operations. Sparse FP16 performance reaches 4500 TFLOPS on the GB300 against 1979 TFLOPS with sparsity on the H100. Such bandwidth and compute ratios favor the GB300 when processing extensive datasets that benefit from high throughput.

The GB300 suits scenarios that require the 2250 TFLOPS dense FP16 or the 4500 TFLOPS FP16 with sparsity. Its 1400W TDP and 8000 GB/s bandwidth make it appropriate for large scale LLM training that utilizes the 262 to 288 GB VRAM capacity.

Choose the H100 when the model fits in 80 to 94 GB and you can accept lower throughput, since it has the same NVLink and InfiniBand fabric for multi-GPU jobs. Choose it outright for FP64 HPC: 34 TFLOPS against 1.25 TFLOPS on the GB300, a 27.2 times gap that Blackwell Ultra gave up by design. Its 1979 TOPS INT8 also far exceeds the 187.5 TOPS of the GB300, which matters only if your inference stack is locked to INT8 rather than FP8 or FP4.