B200 SXM vs MI355X

The B200 outperforms in low-precision computing critical for modern AI inference: its FP8 throughput reaches 9000 TFLOPS, double the MI355X's 4600 TFLOPS, enabling faster processing of quantized models. FP16 performance follows suit at 4500 TFLOPS for the B200 versus 2300 TFLOPS for the MI355X, benefiting training and fine-tuning phases that leverage half-precision arithmetic. However, the B200's FP32 rate drops to 90 TFLOPS, far below the MI355X's balanced 2300 TFLOPS, which suits precision-sensitive simulations. Equal memory bandwidth of 8000 GB/s ensures comparable data movement efficiency, but the MI355X's 288 GB VRAM supports larger batch sizes than the B200's 192 GB, reducing overhead in memory-constrained scenarios like multi-billion parameter model handling. Power draw differs at 1000W TDP for the B200 against 750W for the MI355X, impacting cooling and density in clusters.

Select the B200 for workloads prioritizing raw throughput in FP8 and FP16 formats. Its 9000 TFLOPS FP8 performance accelerates LLM inference, generating tokens twice as fast as the MI355X's 4600 TFLOPS. Availability from $1.71 per hour across 13 cloud offers allows immediate deployment without waiting for the 2025 MI355X release. NVLink and PCIe 6.0 interconnects enhance multi-GPU scaling in SXM form factor setups.

The MI355X suits applications needing extensive VRAM or FP32 balance. Its 288 GB HBM3e capacity handles larger models or batches than the B200's 192 GB, ideal for memory-intensive training. Lower 750W TDP enables higher rack density compared to 1000W, and 2300 TFLOPS FP32 matches FP16 for scientific computing tasks.