B300 vs MI355X

The MI355X records 11 percent higher dense FP16 throughput than the B300 at 2500 TFLOPS versus 2250 TFLOPS. The same pattern holds for sparse FP16 where the MI355X reaches 5000 TFLOPS against 4500 TFLOPS on the B300. These ratios translate into proportionally shorter training step times and higher inference throughput for workloads that operate in FP16 dense or sparse modes. Memory bandwidth stays fixed at 8000 GB/s on both GPUs so maximum batch sizes remain comparable in bandwidth limited regimes. The B300 instead leads in FP4 dense operations with 13500 TFLOPS compared to 10100 TFLOPS on the MI355X. Workloads that rely on FP4 dense or FP8 sparse formats at 9000 TFLOPS on the B300 versus 10100 TFLOPS on the MI355X therefore show different precision specific tradeoffs.

Choose the B300 when you are running CUDA based training and serving stacks and want them to work out of the box, when you plan to scale across many GPUs with NVLink, or when you serve in FP4, where its 13500 TFLOPS dense rate is about 34 percent above the 10100 TFLOPS of the MI355X. It gives up a little dense FP16 (2250 vs 2500 TFLOPS) and dense FP8 (4500 vs 5000 TFLOPS) and almost all FP64 (1.25 vs 78.6 TFLOPS), so it is the wrong pick for double precision HPC.

Choose the MI355X when your stack already runs on ROCm and you want the higher dense FP16 (2500 vs 2250 TFLOPS), dense FP8 (5000 vs 4500 TFLOPS) and dense INT8 (5000 vs 187.5 TOPS) rates, or when the job is double precision, where its 78.6 TFLOPS of FP64 is in a different class from the 1.25 TFLOPS of the B300. It has a fixed 288 GB of HBM3e, the same 8000 GB/s of bandwidth and the same 1400W TDP, so single node capacity is a wash. Multi GPU scale up goes over Infinity Fabric and PCIe 5.0 rather than NVLink, so check that your training framework supports it before booking a multi node run.