MI300X vs. H100 vs. H200 Benchmark Part 1: Training
4 days ago
- #GPU Benchmarking
- #AI Hardware
- #AMD vs Nvidia
- The MI300X has superior on-paper specifications compared to Nvidia's H100 and H200, including higher FLOP/s and memory bandwidth, but real-world performance falls short due to software issues.
- AMD's software stack is riddled with bugs, making out-of-the-box training impossible and requiring extensive tuning and custom builds to achieve usable performance.
- Nvidia's out-of-the-box performance and user experience are superior, with no significant bugs encountered during benchmarking, highlighting the maturity of CUDA and Nvidia's ecosystem.
- AMD's MI300X shows promise in certain benchmarks with custom development builds, but these are not yet merged into the main branch, delaying public availability.
- AMD's scale-out performance is weaker due to inferior ROCm Compute Communication Library (RCCL) and less vertical integration with networking hardware compared to Nvidia's NCCL and InfiniBand/Spectrum-X.
- Many of AMD's AI libraries are forks of Nvidia's, leading to suboptimal performance and compatibility issues.
- Executive recommendations to AMD include increasing investment in software development, improving testing and CI/CD processes, and enhancing the out-of-the-box user experience.
- The MI300X has a lower total cost of ownership (TCO) compared to H100/H200, but training performance per TCO is worse on public stable releases of AMD software.
- Nvidia's NVLink and InfiniBand SHARP technologies provide superior collective communication performance, crucial for large-scale training workloads.
- AMD's user experience is suboptimal, requiring numerous environment flags and custom builds, contrasting with Nvidia's streamlined and user-friendly approach.