Agentic CPU-GPU Scheduling for Heterogeneous AI Workloads

The problem is that existing frameworks assign all GPU-capable AI tools to the GPU by default, which is suboptimal for heterogeneous workloads. The method profiles 19 AI tools across GPU and CPU, formulates device scheduling under a VRAM budget, and presents an agentic scheduler pairing an LLM agent with an algorithmic runtime monitor. Experimental evidence shows the agentic scheduler reaches brute-force optimal mapping in all 13 scenarios, matching the best classical baseline on accuracy while outperforming HEFT, StarPU, and the all-GPU policy. This matters because it enables efficient, zero-training scheduling for agentic AI systems on shared GPU/CPU infrastructure.