Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control
LLM-agent services repeatedly execute small deterministic transitions between model and tool calls. The study formalizes the ready-cohort boundary using fixed-partition share F, exact offline share P*, local upper bound U, and online achieved share A. Under zero service time, unlimited capacity, and equal relative launch deadlines, a specialized dynamic program computes P* exactly. In a stationary Poisson replay of one pinned 851-session public trace panel, the primary condition at 100,000 target active sessions, K=256, and a 50 ms launch deadline gives F=30.19%, P*=43.00%, and U=45.85%. Exact packing recovers 81.83% of the opportunity lost at fixed window boundaries. A separate mechanism study keeps a GPU-computed binary decision on device instead of returning four bytes to the host and redispatching. Across four named GPU placements, the device-resident path is faster in all 36 configurations.
A research paper from arXiv cs.AI, published on 2026-08-12, investigates GPU execution opportunities in LLM-agent control paths. It defines ready-cohort metrics and demonstrates that exact packing recovers 81.83% of opportunity lost at fixed window boundaries under specified conditions. A mechanism study shows that keeping a GPU-computed binary decision on device is faster than returning four bytes to the host and redispatching across all 36 tested configurations.
The ready-cohort boundary formalizes concurrency in LLM-agent transitions. Exact offline share P* is computed via dynamic programming under zero service time and unlimited capacity. The device-resident path avoids host round trips, improving latency across all tested GPU placements.
Optimizing LLM-agent control paths can reduce latency and improve GPU utilization. The findings suggest that on-device decision-making may become a standard pattern for high-throughput agent services.
Reducing host round trips and improving GPU packing can lower operational costs and increase throughput for LLM-agent services, potentially enabling more scalable and responsive AI applications.
Observable next signals include adoption of ready-cohort scheduling in production agent frameworks, further benchmarks on diverse traces, and integration of device-resident routing into inference engines.