Throughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents
Pith reviewed 2026-05-22 21:16 UTC · model grok-4.3
The pith
Work-conserving scheduling algorithms achieve maximum throughput for LLM inference and AI agent workloads.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
A large class of work-conserving scheduling algorithms achieve maximum throughput for both individual requests and AI-agent workloads with directed acyclic graph (DAG) and fork-join routing topologies. The authors develop a fluid-limit framework for multi-class batched processing networks under K-FCFS scheduling to establish this result and to identify work-conserving behavior as a key design principle.
What carries the argument
Fluid-limit framework for multi-class batched processing networks under K-FCFS scheduling, used to prove stability at maximum throughput for work-conserving policies across the stated request and agent routing topologies.
If this is right
- Work-conserving schedulers are throughput-optimal for processing streams of individual LLM requests.
- Work-conserving schedulers are throughput-optimal for AI-agent workloads that follow DAG and fork-join routing.
- Orca and Sarathi-Serve achieve maximum throughput under the model.
- FasterTransformer and vanilla vLLM do not achieve maximum stability and should be used with caution.
- Batch-size limits and cyclic routing topologies lie outside the optimality guarantee and require separate analysis.
Where Pith is reading between the lines
- New LLM serving platforms should embed work-conserving logic as a default to secure high throughput.
- The same fluid model could be applied to test stability of other batched AI workloads that share similar routing patterns.
- Explicit measurement of idle time under work-conserving rules in large clusters would provide a direct empirical check.
- Extensions that incorporate hardware-specific preemption costs could tighten the conditions under which the optimality holds.
Load-bearing premise
The fluid-limit model of multi-class batched processing networks under K-FCFS scheduling faithfully represents the stability behavior of real LLM inference systems, including batching and preemption effects.
What would settle it
A demonstration that any work-conserving scheduler fails to stabilize an LLM inference system or AI-agent workload at the maximum throughput rate under high load, whether in simulation or live deployment, would disprove the optimality claim.
read the original abstract
As demand for Large Language Models (LLMs) and AI agents grows rapidly, optimizing systems for efficient LLM inference becomes critical. While significant efforts have targeted system-level engineering, little has been explored from a mathematical modeling and queueing perspective. In this paper, we develop the queueing fundamentals for LLM inference. In particular, we study the throughput aspect of LLM inference systems. We prove that a large class of `work-conserving' scheduling algorithms achieve maximum throughput for both individual requests and AI-agent workloads with directed acyclic graph (DAG) and fork-join routing topologies, establishing `work-conserving' as a key design principle for practitioners. Technically, we develop a fluid-limit framework for multi-class batched processing networks under $K$-FCFS scheduling, which may be of independent interest. Evaluations of real-world systems confirm that Orca and Sarathi-Serve are throughput-optimal, reassuring practitioners, while FasterTransformer and vanilla vLLM are not maximally stable and should be used with caution. Our analysis also reveals how constraints such as batch size limits and cyclic routing topologies complicate the throughput picture, pointing to rich open questions at the intersection of queueing theory and LLM system design.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims to develop queueing fundamentals for LLM inference, proving via a fluid-limit framework for multi-class batched processing networks under K-FCFS (and work-conserving variants) that a large class of work-conserving scheduling algorithms achieve maximum throughput for both individual requests and AI-agent workloads with DAG and fork-join topologies. Real-system evaluations are said to confirm Orca and Sarathi-Serve as throughput-optimal while FasterTransformer and vanilla vLLM are not; batch-size limits and cyclic routing are noted as complicating factors.
Significance. If the fluid-limit stability results transfer rigorously to the discrete LLM setting, the work would establish work-conserving policies as a practical design principle and introduce a potentially reusable fluid model for batched multi-class networks with DAG/fork-join routing. The combination of theoretical claims with system evaluations is a strength.
major comments (2)
- [Abstract] Abstract and technical framework: the central claim that fluid-limit stability under K-FCFS implies throughput optimality in real LLM systems rests on the unproven transfer from continuous fluid model to discrete systems with finite batch sizes, KV-cache constraints, and preemption (e.g., Orca-style); the abstract itself flags that batch-size limits complicate the picture, but no explicit conditions or counter-example analysis for fork-join synchronization delays are provided.
- [Fluid-limit framework] Fluid model section (implied by abstract): the multi-class batched processing network model under K-FCFS must be shown to capture idling or synchronization at fork-join points induced by discrete batching; without this, the stability region for AI-agent DAG workloads may be overstated relative to actual systems.
minor comments (1)
- [Abstract] The abstract would benefit from a one-sentence definition or reference for K-FCFS scheduling to aid readers unfamiliar with the variant.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback highlighting the gap between our fluid-limit analysis and discrete LLM system realities. We respond point-by-point to the major comments and indicate the revisions we will undertake.
read point-by-point responses
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Referee: [Abstract] Abstract and technical framework: the central claim that fluid-limit stability under K-FCFS implies throughput optimality in real LLM systems rests on the unproven transfer from continuous fluid model to discrete systems with finite batch sizes, KV-cache constraints, and preemption (e.g., Orca-style); the abstract itself flags that batch-size limits complicate the picture, but no explicit conditions or counter-example analysis for fork-join synchronization delays are provided.
Authors: We agree that the manuscript does not contain a rigorous proof establishing that fluid-limit stability under K-FCFS transfers to throughput optimality in discrete systems subject to finite batch sizes, KV-cache constraints, and preemption. The fluid model serves as an asymptotic approximation. We will revise the abstract to qualify the optimality claim as holding in the fluid limit and add a dedicated subsection that states explicit conditions under which the results are expected to extend, together with a simple counter-example illustrating how finite batch sizes can induce extra synchronization delay at fork-join points. revision: yes
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Referee: [Fluid-limit framework] Fluid model section (implied by abstract): the multi-class batched processing network model under K-FCFS must be shown to capture idling or synchronization at fork-join points induced by discrete batching; without this, the stability region for AI-agent DAG workloads may be overstated relative to actual systems.
Authors: The current fluid model encodes fork-join synchronization by requiring all incoming fluid flows to reach the join server before further progress; K-FCFS then processes the combined batch in a work-conserving fashion. This formulation captures synchronization in the continuous limit. We acknowledge, however, that discrete batching can create additional idling not visible in the fluid equations. We will expand the fluid-model section with explicit fluid-balance equations for DAG and fork-join topologies and add a paragraph discussing the approximation gap that arises from discrete batching. revision: partial
Circularity Check
No circularity: standard queueing analysis applied to new model
full rationale
The paper introduces a fluid-limit framework for multi-class batched processing networks under K-FCFS and proves stability for work-conserving policies on DAG and fork-join topologies using established queueing-theoretic methods. The throughput-optimality claims follow directly from these fluid-model stability results transferred to LLM inference workloads. No equations reduce to self-defined quantities, no parameters are fitted and then relabeled as predictions, and no load-bearing steps rely on self-citations that themselves assume the target result. The framework is explicitly noted as potentially of independent interest, and the paper separately acknowledges real-system complications such as batch-size limits without claiming the fluid model is exact. This is a self-contained derivation.
Axiom & Free-Parameter Ledger
axioms (1)
- standard math Fluid-limit analysis yields valid stability conditions for multi-class batched processing networks under K-FCFS scheduling.
Forward citations
Cited by 5 Pith papers
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A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
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discussion (0)
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