GPU time-slicing is attractive for teams trying to run multiple LLM agents on shared Kubernetes infrastructure. A new systems deep dive looks at what that sharing really costs, including the hidden microarchitectural effects that can appear when agentic workloads compete for the same accelerator.
The takeaway is that utilization is not the only metric that matters. For production AI infrastructure, teams need to understand latency, context switching, memory pressure, and workload interference before assuming that denser packing automatically lowers costs.