TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving
TAO is a tool-aware joint scheduling framework for LLM-based agentic serving, where sessions alternate between GPU inference and external CPU tools. It pauses sessions during tool calls, scores session-node pairs using near-term tool readiness, time-decayed cache credit, and token footprint, and uses a dynamic program to jointly select and place waiting sessions under GPU capacity limits. Across 12 reported model-workload-concurrency settings, the paper reports latency reductions up to 26.8%, mean TTFT reductions up to 36.3%, and decode throughput gains up to 29.5% versus the best evaluated baseline per metric.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
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TAO is a tool-aware joint scheduling framework for LLM-based agentic serving, where sessions alternate between GPU inference and external CPU tools. It pauses sessions during tool calls, scores session-node pairs using near-term tool readiness, time-decayed cache credit, and token footprint, and uses a dynamic program to jointly select and place waiting sessions under GPU capacity limits. Across 12 reported model-workload-concurrency settings, the paper reports latency reductions up to 26.8%, mean TTFT reductions up to 36.3%, and decode throughput gains up to 29.5% versus the best evaluated baseline per metric.
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- 04What could mislead?
Source claims not independently checked; reported gains are relative to the best evaluated baseline per metric and apply to the paper's specific settings.
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Key findings from the stored brief
- TAO pauses sessions during external tool calls to free GPU capacity and places them into a shared waiting pool.
- Session valuation combines near-term tool-return readiness, time-decayed KV-cache credit, and token footprint without manual weight tuning.
- A joint dynamic program replaces greedy per-session routing, reported at under 0.12% runtime overhead.
- Reported gains reach up to 26.8% lower latency, 36.3% lower mean TTFT, and 29.5% higher decode throughput.
Source claims not independently checked; reported gains are relative to the best evaluated baseline per metric and apply to the paper's specific settings.
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Why this may matter
Agentic workloads waste GPU capacity when they idle during tool calls and rely on heuristic admission and placement. A scheduler that accounts for tool-return timing and cache state could improve serving efficiency under tight memory budgets.
Limits and open questions
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