Case Study: Hippocratic AI achieves sub-second patient conversation latency with Modular

Hippocratic AI cuts end-to-end latency by 30% with Modular

Hippocratic AI builds safety-focused AI health agents that converse with patients in real-time. Their challenge was that existing inference frameworks could not maintain the required low latency as they scaled, risking unnatural conversations. They partnered with Modular to integrate its MAX framework to power their Polaris system.

By implementing Modular's MAX, Hippocratic AI achieved sub-second time to first token and significantly faster end-to-end latency. This performance improvement ensured natural patient interactions. The solution also provided hardware flexibility, allowing deployment across NVIDIA and AMD GPUs, which unlocked a previously unattainable heterogeneous deployment strategy.

Hippocratic AI