GPU Time-Slicing for Concurrent LLM Agents on Kubernetes

This article explores the microarchitectural costs of GPU time-slicing in Kubernetes when running concurrent LLM agents, revealing hidden performance overheads. It provides a systems-level analysis of the trade-offs involved in co-locating agentic AI workloads on shared GPU resources. The findings highlight the practical challenges and inefficiencies of using time-slicing for concurrent agent deployments.