MI250X vs MI300X

Dense FP16 performance reaches 383 TFLOPS on the MI250X and 1307.4 TFLOPS on the MI300X. The FP32 figures produce an identical ratio of approximately eight times higher dense FP16 throughput on each device. This ratio indicates both GPUs allocate similar hardware emphasis to reduced precision matrix operations that dominate training and inference passes. Memory bandwidth of 5300 GB/s on the MI300X versus 3277 GB/s on the MI250X permits larger batch sizes before memory stalls occur during token processing. Dense INT8 throughput stands at 383 TOPS for the MI250X and 2614.9 TOPS for the MI300X. The bandwidth delta therefore translates into higher sustained utilization when batch dimensions exceed the capacity supported by 3277 GB/s.

The MI250X fits deployments where total power draw must remain at 560W and model memory requirements stay inside 128 GB. Scientific computing codes that already achieve target throughput at 47.9 FP32 TFLOPS benefit from the lower TDP without requiring the additional 192 GB capacity. Workloads that run exclusively in dense modes see no sparsity advantage on the newer part yet incur the higher 750W envelope.

The MI300X suits training and inference jobs that exceed 128 GB per accelerator or that scale batch sizes with 5300 GB/s bandwidth. Dense FP16 throughput of 1307.4 TFLOPS combined with 192 GB HBM3 memory supports larger context lengths than the MI250X configuration allows. Applications that can utilize sparsity reach 2614.9 TFLOPS in FP16 while remaining within the same OAM form factor.