H200 SXM vs MI300X

FP16 performance defines training efficiency for deep learning models: H200 achieves 1979 TFLOPS, outpacing MI300X's 1307 TFLOPS by 51 percent, which accelerates mixed-precision LLM training cycles. Similarly, H200's 3958 TFLOPS FP8 doubles MI300X's 2614 TFLOPS, enabling higher throughput in quantized inference scenarios with reduced latency. These advantages suit NVIDIA-optimized frameworks like CUDA.

MI300X dominates FP32 workloads at 163 TFLOPS versus H200's 67 TFLOPS, more than doubling capacity for scientific simulations and precise floating-point computations. Memory specs further differentiate them: MI300X's 192 GB VRAM supports larger models or batch sizes without multi-GPU sharding, while its 5300 GB/s bandwidth exceeds H200's 4800 GB/s to minimize data transfer bottlenecks in memory-bound tasks. Power draw remains close, with H200 at 700W TDP and MI300X at 750W.

Real-world impacts include H200 favoring speed-critical inference pipelines, whereas MI300X enables handling of massive datasets in a single GPU, potentially lowering interconnect overhead.

Select the H200 for FP16 and FP8 intensive applications like high-volume LLM inference, where its 3958 TFLOPS FP8 delivers unmatched quantized performance. Training pipelines benefit from 1979 TFLOPS FP16, reducing epoch times in NVIDIA-centric ecosystems with NVLink interconnects. Cloud users prioritizing raw throughput over capacity find its 23 rental offers advantageous despite higher $3.70 hourly average.

The MI300X suits memory-constrained workloads such as loading 100 billion parameter models into 192 GB VRAM, avoiding the H200's 141 GB limit. FP32-heavy scientific computing leverages its 163 TFLOPS, far exceeding H200's 67 TFLOPS. Budget-conscious deployments favor its $0.50 per hour starting price and 5300 GB/s bandwidth for sustained large-batch processing.