B300 SXM6 vs MI300X

The B300 SXM6 delivers 2250 TFLOPS FP16 dense compared with 1307.4 TFLOPS FP16 dense on the MI300X, producing a 1.72 times advantage in dense matrix operations for training workloads. The same pattern holds for FP16 with sparsity at 4500 TFLOPS versus 2614.9 TFLOPS. Memory bandwidth of 8000 GB/s versus 5300 GB/s permits larger batch sizes during inference and training on the B300 SXM6. FP32 throughput favors the MI300X at 163.4 TFLOPS against 75 TFLOPS yet tensor workloads dominate modern AI pipelines where the B300 SXM6 maintains higher dense and sparsity figures across FP8 and FP4 as well.

The B300 SXM6 suits deployments that require maximum memory capacity of 262 GB and highest tensor throughput of 2250 TFLOPS FP16 dense. Its SXM6 form factor and NVLink interconnect support dense multi GPU scaling for large model training. Higher TDP of 1400 W is accepted when absolute performance per GPU takes priority over power draw.

The MI300X suits power constrained environments limited to 750 W TDP while still delivering 192 GB memory and 2614.9 TFLOPS FP8 dense. Its OAM form factor and Infinity Fabric interconnect fit existing AMD server platforms focused on energy efficiency. FP32 performance of 163.4 TFLOPS provides an edge in scientific computing codes that rely less on tensor cores.