In algorithmic trading, reducing response times to market events is crucial. To keep pace with high-speed electronic markets, latency-sensitive firms often use specialized hardware like FPGAs and ASICs. Yet, as markets grow more efficient, traders increasingly depend on advanced models such as deep neural networks to enhance profitability. Because implementing these complex models on low-level hardware requires significant investment, general-purpose GPUs offer a practical, cost-effective alternative.  The NVIDIA GH200 Grace Hopper Superchip in the Supermicro ARS-111GL-NHR server has achieved single-digit microsecond latencies in the STAC-ML Markets (Inference) benchmark, Tacana suite (audited by STAC), providing performance comparable to or better than specialized hardware systems.  This post details these record-breaking results and provides a deep dive into the custom-tailored solutions required for low-latency GPU inference.…