Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control

arXiv:2608.12123v2 Announce Type: replace-cross Abstract: We introduce a mechanism that improves LLM-agent execution: keeping four-byte, GPU-computed control-path routing decisions on-device, avoiding a round-trip to host memory for redispatch. It is faster than host-dispatch in all 36 placements x mechanisms settings in our benchmark across four named GPUs with row-median speedup of 1.19x-2.39x, and outputs are correct, matching an independent host implementation, for all 14,557,440 calls in all configurations, in both tested mechanisms. Agent control paths can be deterministic. Their transitions between LLM calls and external tool calls can be GPU-accelerated by batching across agents and time. We define a cohort of transitions with launch times in a window, and study four shares of GPU-active time: a fixed-partition baseline F, an optimal P*, an upper bound U, and an online result A. Under assumptions of uniform relative launch deadlines, infinite capacity, and zero service times, we compute P* with a custom dynamic program and validate our simulator against it. We replay an anonymized 851-session trace, shown in full as a panel, stationary-ized with a Poisson process. With 100,000 target active agents and a launch deadline of 50 ms, where the headline results have K=256, we find F=30.19%, P*=43.00%, U=45.85%; our method for computing P* reveals that 81.83% of this opportunity is recoverable from cohort packing at fixed window ends. Our method for U relies on a novel notion of route-key, used only as a proxy (via conditional statistics) and does not purport to establish route-equivalence. We find a nested graph that retains some host-side decisions slower in all 60 configurations (five placements).

Abstract:We introduce a mechanism that improves LLM-agent execution: keeping four-byte, GPU-computed control-path routing decisions on-device, avoiding a round-trip to host memory for redispatch. It is faster than host-dispatch in all 36 placements x mechanisms settings in our benchmark across four named GPUs with row-median speedup of 1.19x-2.39x, and outputs are correct, matching an independent host implementation, for all 14,557,440 calls in all configurations, in both tested mechanisms. Agent control paths can be deterministic. Their transitions between LLM calls and external tool calls can be GPU-accelerated by batching across agents and time. We define a cohort of transitions with launch times in a window, and study four shares of GPU-active time: a fixed-partition baseline F, an optimal P*, an upper bound U, and an online result A. Under assumptions of uniform relative launch deadlines, infinite capacity, and zero service times, we compute P* with a custom dynamic program and validate our simulator against it. We replay an anonymized 851-session trace, shown in full as a panel, stationary-ized with a Poisson process. With 100,000 target active agents and a launch deadline of 50 ms, where the headline results have K=256, we find F=30.19%, P*=43.00%, U=45.85%; our method for computing P* reveals that 81.83% of this opportunity is recoverable from cohort packing at fixed window ends. Our method for U relies on a novel notion of route-key, used only as a proxy (via conditional statistics) and does not purport to establish route-equivalence. We find a nested graph that retains some host-side decisions slower in all 60 configurations (five placements).

14 pages, 4 figures. Includes formal proofs, trace provenance, and a reproducibility appendix. Code and artifacts: this https URL ; processed evidence: this https URL

Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Operating Systems (cs.OS)

arXiv:2608.12123 [cs.DC]

(or arXiv:2608.12123v2 [cs.DC] for this version)

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Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control

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