OS-Level Challenges in LLM Inference and Optimizations
Large Language Model (LLM) inference pushes computing systems to their limits, not only in raw compute but also in how the operating system (OS) manages resources. This report examines OS-level challenges for LLM inference and explores potential solutions. We focus first on key bottlenecks – memory management, CPU scheduling, I/O, and real-time constraints – then discuss how kernel-level techniques (like eBPF and custom scheduling) can address these issues. We also consider the impact of system calls and page faults on performance, security/isolation concerns in multi-tenant environments, best practices and emerging research in OS customization for AI, and practical considerations for implementing such optimizations in a research project. The goal is to guide a research initiative by outlining the challenges and then potential solutions and areas for further investigation.