2025

As GPU vendors supporting heterogeneous architectures continue to multiply, their software and hardware ecosystems remain fragmented—effectively forming isolated “silos.” Rather than forcing tight hardware-level co-execution (“hard coordination”), the path forward lies in software-level interoperability: empowering flexible deployment and efficient scheduling of heterogeneous inference workloads through a universal, vendor-agnostic communication library.

Recently, I’ve taken some time to review recent research papers to explore emerging trends in eBPF and reflect on its evolving landscape. In this article, I focus on the growing trend of using eBPF for system acceleration. Specifically, I will discuss three key areas:

• Network performance optimization,
• System I/O acceleration, and
• Observability with minimal overhead.

With the rapid development of machine learning and deep learning, model management and deployment have become important and complex challenges. To simplify this process, we introduce OneInfer, an open-source command-line tool designed to help users efficiently manage and serve various types of machine learning models.