Open Source Summit North America 2025

AI models are evolving rapidly, while also growing exponentially in size and complexity. As AI workloads become larger, it is crucial to address the challenges of rapidly scaling inference services during peak hours and how to ensure optimal GPU utilization for fine-tuning workloads. To tackle this, Bloomberg’s Data Science Platform team has implemented a “Model Cache” feature in the open source KServe project for caching large models on GPUs in a multi-cloud and multi-cluster cloud-native environment.

This talk discusses the challenges faced with hosting large models for inference and fine-tuning purposes, and how model caching can help mitigate some of these challenges by reducing load times during auto-scaling of services, improving resource utilization, and boosting data scientists’ productivity. The talk dives into how Bloomberg integrated KServe’s Model Cache into its AI workloads and built an API on top of Karmada to manage cache federation. AI infrastructure engineers will learn about the profound impact of enabling model caching and how teams can adopt this feature in their own AI infrastructure environment.