GH200 vs MI325X

Peak FP16 performance favors the GH200 at 1979 TFLOPS over MI325X's 1307 TFLOPS, accelerating low-precision matrix multiplications in LLM training. The GH200's FP8 reaches 3958 TFLOPS, doubling MI325X's 2614 TFLOPS for inference quantization. However, MI325X balances FP32 at 1307 TFLOPS against GH200's 67 TFLOPS, suiting simulations requiring single-precision accuracy.

Memory capacity and bandwidth define large-model handling: MI325X's 256 GB HBM3e versus 96 GB HBM3 enables larger batch sizes without swapping, while 6000 GB/s bandwidth exceeds GH200's 4000 GB/s to reduce latency in data-intensive feeds. This impacts training throughput for models exceeding 100 billion parameters.

Power efficiency tilts toward MI325X at 750 W TDP compared to 900 W, potentially lowering operational costs in dense racks. Form factors differ with SXM for GH200 and OAM for MI325X, affecting integration. Real-world inference benefits from GH200's precision peaks, but MI325X supports extended context lengths via superior memory.

Opt for the GH200 in FP16-heavy workloads like transformer training where 1979 TFLOPS outperforms MI325X's 1307 TFLOPS. NVLink-C2C interconnect scales efficiently across multi-GPU setups for distributed training. Immediate cloud access at $1.99 per hour average $3.59 per hour suits rapid prototyping.

Hopper architecture maturity aids software ecosystems with optimized CUDA libraries for AI frameworks.

Select MI325X for memory-bound tasks leveraging 256 GB HBM3e versus 96 GB, ideal for inference on models with long contexts. Superior 6000 GB/s bandwidth and 1307 TFLOPS FP32 handle scientific computing or fine-tuning large datasets. Lower 750 W TDP enhances density in power-constrained environments.

Infinity Fabric supports AMD-optimized ROCm stacks for emerging workloads.