GPU Time-Slicing for Concurrent LLM Agents on Kubernetes

# Running Multiple AI Agents on Shared Hardware Gets Expensive When companies try to save money by running several AI agents on the same GPU hardware at the same time, the system spends a lot of energy switching between them—kind of like how a single cashier gets slower when trying to help multiple customers simultaneously. This technical overhead can eat up a significant portion of your computing resources, making the cost savings vanish. Understanding these hidden costs matters if you're planning to deploy AI agents and want to know whether sharing expensive hardware will actually save money or just create bottlenecks.