B300 SXM6 vs MI325X

B300's FP16 performance reaches 2250 TFLOPS, doubling MI325X's 1307 TFLOPS and enabling faster AI training where half-precision arithmetic prevails. The FP32 gap is pronounced: MI325X delivers 1307 TFLOPS while B300 manages only 90 TFLOPS, favoring MI325X for precision-dependent simulations and certain inference pipelines. FP8 capabilities follow suit, with B300 at 4500 TFLOPS versus 2614 TFLOPS on MI325X, benefiting quantized inference on B300. Memory bandwidth of 12000 GB/s on B300 supports larger batch sizes in training compared to 6000 GB/s on MI325X, reducing data loading bottlenecks for massive datasets. The 288 GB VRAM on B300 accommodates larger models without offloading, unlike MI325X's 256 GB limit, which impacts long-context inference efficiency.

B300 suits memory-intensive workloads like large-scale LLM training. Its 288 GB VRAM and 12000 GB/s bandwidth handle models exceeding 256 GB contexts without performance degradation. FP16 throughput of 2250 TFLOPS accelerates iterations in deep learning pipelines. For inference on quantized models, 4500 TFLOPS FP8 provides superior speed. NVLink interconnect enhances multi-GPU scaling in SXM form factor.

MI325X fits power-sensitive deployments with 750W TDP versus B300's 1200W. Balanced FP16 and FP32 at 1307 TFLOPS each excel in hybrid precision tasks such as scientific simulations. Infinity Fabric interconnect supports dense OAM configurations. Lower bandwidth of 6000 GB/s suffices for workloads not bottlenecked by memory access.