LLM agents automate GPU kernel optimization by repeatedly composing candidates and measuring their duration on real GPUs. Existing systems preserve measurement fidelity by reserving a GPU for an entire agent session or benchmarking command. However, this results in poor utilization because only a small fraction of comm...
Tian-Yu Feng, Hao-Xuan Yu, Tian-Yuan Wu et al.· 0 citations
ServerlessT2I is presented, a serverless-native system that decomposes a T2I workflow into loosely coupled model functions that can be independently managed and scheduled and introduces a fair scheduler for multi-tenant serving.
Xiao-Xiao Jiang, Su-Yi Li, Sheng Yao et al.· arXiv.org· 0 citations
This work presents AgentSysBench, a benchmark suite and measurement toolkit with ten representative agentic applications and unified systems-level instrumentation, and identifies six properties that distinguish agentic workloads from conventional LLM serving.
Chaokun Chang, Yu-Kun Zhou, Kai-Hua Fu et al.· 11 citations
Rollplex is presented, a runtime that decomposes the reference and training phase and moves the prefix computation into the rollout decode window and achieves speedup over serial colocation and disaggregation under the same GPU budget, while preserving the synchronous RL update.
Han-Feng Lu, Tian-Yu Feng, Su-Yi Li et al.· 1 citation
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