H200 vs MI325X

H200's FP16 performance of 1979 TFLOPS exceeds MI325X's 1307 TFLOPS, providing faster matrix multiplications in training workflows that rely on half-precision arithmetic. The FP32 disparity is stark: MI325X delivers 1307 TFLOPS versus H200's 67 TFLOPS, benefiting simulations and scientific computing where single-precision is standard. FP8 at 3958 TFLOPS on H200 doubles MI325X's 2614 TFLOPS, accelerating inference on quantized models.

Memory capacity and bandwidth define real-world scalability: MI325X's 256 GB HBM3e and 6000 GB/s allow larger batch sizes in LLM inference, reducing latency for models exceeding 141 GB like certain 405B-parameter LLMs. H200's 4800 GB/s suffices for most current workloads but limits contexts in memory-bound scenarios. TDP differences are minor at 700W for H200 and 750W for MI325X, with power efficiency favoring H200 in FP16-dominated tasks.

Training favors H200 due to superior FP16 throughput, while MI325X excels in inference with its memory advantage, enabling higher throughput for deployment-scale serving.

Opt for the H200 in NVIDIA-centric environments requiring immediate availability and high FP16 performance of 1979 TFLOPS. Its 26 live cloud offers from $0.50 per hour suit rapid prototyping and training of models under 141 GB VRAM, leveraging NVLink for multi-GPU scaling.

H200 is ideal for FP8 inference at 3958 TFLOPS, where software optimizations like TensorRT provide edge over AMD ecosystems.

Select MI325X for workloads demanding extreme memory: 256 GB HBM3e handles unpartitioned massive models, and 6000 GB/s bandwidth supports large-batch inference.

Balanced FP32 at 1307 TFLOPS makes it preferable for HPC tasks like molecular dynamics, where NVIDIA's 67 TFLOPS FP32 falls short.