Delivering the AI Edge for High-Frequency Trading

In recent years, the financial sector has witnessed a significant surge in the adoption of artificial intelligence (AI), with high-frequency trading (HFT) firms leading the charge. As of 2024, 72% of global organizations have integrated AI into at least one business function, a sharp rise from 55% the previous year (McKinsey Report). Firms like Citadel, Virtu, DRW, and Jump Trading are heavily investing in AI-driven models to optimize their trading strategies.

To maximize return on investment (ROI), it is critical to ensure efficient utilization of AI and computing resources. The highest cost in this equation is GPU efficiency— with traditional AI deployments achieving only 70% efficiency due to inefficient data pipelines. This paper explores the architecture of an AI-driven intelligence platform for HFT and its design considerations for high-performance AI workflows.