H200 SXM vs MI355X

Memory specifications define key advantages: the MI355X's 288 GB HBM3e VRAM versus the H200's 141 GB supports larger batch sizes and full model loading for massive LLMs, minimizing communication overhead in multi-GPU setups. The MI355X's 8000 GB/s bandwidth exceeds the H200's 4800 GB/s by 67%, accelerating data transfers and enabling higher throughput in memory-bound workloads like training with large datasets.

Compute performance reveals nuanced differences. The MI355X delivers 2300 TFLOPS in FP16 and an identical 2300 TFLOPS in FP32, offering balanced precision for training pipelines that mix tensor cores and matrix operations. The H200 provides 1979 TFLOPS FP16 but only 67 TFLOPS FP32, a 34x gap that limits it in FP32-dominant scientific simulations. For FP8 inference, the MI355X's 4600 TFLOPS edges the H200's 3958 TFLOPS by 16%, suiting quantized model deployments.

Power draw remains close at 750W for MI355X and 700W for H200, suggesting similar density in racks. These specs translate to MI355X favoring memory-intensive training with sustained FP32 needs, while H200 suits FP16-heavy inference where software optimization matters.

Opt for the NVIDIA H200 SXM when immediate deployment is critical. It features 22 live cloud offers from $1.19 per hour, averaging $3.81 per hour, enabling quick scaling without waiting for MI355X availability. The H200's NVLink, PCIe 5.0, and InfiniBand interconnects integrate seamlessly with mature NVIDIA CUDA ecosystems for established AI pipelines.

Choose H200 for workloads optimized around Hopper architecture, such as FP16-dominant inference at 1979 TFLOPS, where its 4800 GB/s bandwidth suffices and software maturity reduces development time.

Select the AMD Instinct MI355X for future-proofing massive AI models requiring 288 GB VRAM, far exceeding the H200's 141 GB. This capacity supports single-GPU loading of models over 100 billion parameters, streamlining inference and training.

MI355X excels in balanced compute scenarios with 2300 TFLOPS across FP16 and FP32, plus 8000 GB/s bandwidth for high-batch training. Infinity Fabric suits AMD-centric clusters planning CDNA 4 adoption.