H200 vs MI355X

Memory specifications create the largest real-world impact: MI355X's 288 GB HBM3e VRAM surpasses H200's 141 GB, supporting larger models or batch sizes in LLM training without model parallelism. The 8000 GB/s bandwidth exceeds H200's 4800 GB/s, minimizing stalls in memory-intensive operations like gradient accumulation during training.

Compute differences favor MI355X in mixed-precision scenarios. FP16 performance of 2300 TFLOPS edges H200's 1979 TFLOPS, benefiting transformer training where half-precision dominates. FP8 at 4600 TFLOPS tops 3958 TFLOPS, aiding efficient inference. The FP32 disparity is profound: 2300 TFLOPS versus 67 TFLOPS enables MI355X for FP32-bound tasks such as scientific simulations, while H200 prioritizes low-precision AI acceleration.

Power draw is close at 750W TDP for MI355X and 700W for H200, but interconnects vary: H200 supports NVLink, PCIe 5.0, and InfiniBand for scalable clusters, versus MI355X's Infinity Fabric. These factors determine throughput in distributed training versus single-node inference.

The H200 excels in production environments requiring immediate deployment. With 26 live cloud offers starting at $0.50 per hour and averaging $3.62 per hour, it provides cost-effective access today. NVIDIA's mature ecosystem, including NVLink and InfiniBand interconnects, ensures reliable multi-GPU scaling for current Hopper-optimized software stacks.

Choose H200 for workloads leveraging its 1979 TFLOPS FP16 and 3958 TFLOPS FP8, such as ongoing inference services where availability trumps future specs.

The MI355X stands out for memory-constrained applications once available. Its 288 GB HBM3e VRAM and 8000 GB/s bandwidth handle massive datasets or batch sizes infeasible on H200's 141 GB and 4800 GB/s. High FP32 performance of 2300 TFLOPS suits simulations beyond H200's 67 TFLOPS.

Select MI355X for forward-looking setups prioritizing CDNA 4 architecture and OAM form factor in Infinity Fabric clusters.