H200 vs MI300X

The H200 demonstrates clear advantages in low-precision compute critical for modern AI: FP16 reaches 1979 TFLOPS compared to 1307 TFLOPS on the MI300X, accelerating training phases that rely on mixed-precision arithmetic. FP8 performance further favors the H200 at 3958 TFLOPS versus 2614 TFLOPS, enhancing inference throughput for quantized models. These deltas translate to faster iterations in LLM training, where FP16 dominates.

FP32 performance reverses the trend: the MI300X delivers 163 TFLOPS against 67 TFLOPS on the H200, benefiting scientific simulations or legacy workloads requiring single-precision accuracy. Memory specs also differ significantly: 192 GB VRAM on the MI300X supports larger batch sizes or models than 141 GB on the H200, while 5300 GB/s bandwidth versus 4800 GB/s reduces bottlenecks in data-heavy tasks, enabling 10-20% higher throughput in memory-bound scenarios.

Power draw is close, with the H200 at 700W TDP and MI300X at 750W, but interconnects vary: NVLink on H200 aids multi-GPU scaling over Infinity Fabric on MI300X.

Opt for the H200 in FP16 and FP8 dominant workloads such as LLM inference and training, where 1979 TFLOPS FP16 and 3958 TFLOPS FP8 outperform the MI300X's 1307 TFLOPS and 2614 TFLOPS. Availability across 9 cloud providers from $0.49 per hour makes it practical for immediate deployment in SXM or NVL form factors with NVLink support.

Its Hopper architecture suits NVIDIA-optimized software stacks, reducing development time for teams leveraging CUDA ecosystems.

Choose the MI300X for memory-intensive applications needing 192 GB HBM3 VRAM over the H200's 141 GB, such as handling massive datasets in fine-tuning or scientific computing. Higher 5300 GB/s bandwidth and 163 TFLOPS FP32 suit large-batch training or simulations where precision matters more than low-precision speed.

OAM form factor and Infinity Fabric interconnect appeal to AMD-centric environments, despite current lack of live cloud pricing.