Inference Benchmarks: Navigating the AI Hardware Landscape

In an era where generative AI defines competitive advantage, infrastructure decisions have shifted from operational necessities to strategic imperatives. This interactive benchmarking tool aggregates rigorous performance data from MLCommons (MLPerf®), the industry standard for measuring AI system performance. By synthesizing results across the latest hardware architectures, including NVIDIA’s Hopper (H100/H200) and Blackwell (B200) series,  we provide a granular view of inference throughput for defining foundation models such as Llama 2, Llama 3, DeepSeek R1, and Stable Diffusion.

Navigating the trade-offs between latency, throughput, and total cost of ownership (TCO) requires precise, comparable data. This tool empowers enterprise architects and CTOs to dissect performance metrics across distinct scenarios (Offline vs. Server) and hardware configurations. Whether evaluating the token-per-second capabilities of HGX systems or the efficiency of edge-optimized L40S deployments, these benchmarks serve as a critical baseline for right-sizing AI clusters. By isolating variables such as accelerator count, node topology, and processor architecture, organizations can move beyond marketing claims to engineer infrastructure that aligns perfectly with their specific workload requirements.

Disclaimer: All figures generated by this tool are estimates based MLPerf®  data and benchmarks. While best efforts are made to ensure data accuracy, errors may occur. We disclaim liability for any damages or losses resulting from the use of or reliance on the information. Please use these results at your own discretion and at your own risk.