>1B tokens/minute/GPU by combining query planner and inference engine (modal.com)

A significant breakthrough in AI inference has been achieved with the introduction of the QUery-Aware Inference Layer (Quail), which processes over a billion tokens per minute on a single H100 GPU. This development stems from a collaboration between Modal and Carnegie Mellon University's Full Stack Data Lab, addressing the growing demand for efficient inference in AI-SQL applications. By integrating a query planner with an inference engine, Quail optimizes the processing of AI-SQL queries, enabling high-speed and low-cost operations at less than 6 cents per billion tokens. The importance of Quail lies in its ability to harness the structured nature of SQL queries to better manage key-value (KV) caches and avoid inefficiencies inherent in traditional inference methods. Unlike typical AI applications that focus on user-generated prompts, AI-SQL allows for more structured queries that can strategically anticipate data needs. This approach not only improves GPU utilization but also shifts the focus from the complex decode phase to a simpler prefill phase, making inference faster and more efficient. As the AI/ML community increasingly shifts toward such backend optimizations, Quail's architecture sets the stage for future performance gains in AI-driven applications.