LLM Inference Scheduling Overview

Updated 22 August 2025

LLM inference scheduling is a framework that allocates resources and batches LLM requests to manage memory constraints and variable output lengths effectively.
Key advances include predictive scheduling methods like Aₘᵢₙ and Aₘₐₓ, which adjust allocations based on token prediction intervals and workload dynamics.
Integrative models combine memory management, fairness, and distributed resource allocation to double throughput and cut latency in production deployments.

LLM inference scheduling is the process of managing, batching, and allocating system resources to concurrent requests for text generation from LLMs, with the goal of optimizing throughput, latency, resource efficiency, and quality of service (QoS). Unlike classical job scheduling, LLM inference presents unique challenges due to the sequential and memory-intensive nature of autoregressive token generation, unknown or imprecise output lengths for each request, significant GPU memory constraints (especially from growing key–value caches), frequent heterogeneous service requirements, and rapidly fluctuating workloads. Recent research advances have produced highly specific models, algorithms, and theoretical frameworks to address these challenges in both single-node and distributed, multi-tenant deployments.

1. Fundamental Constraints in LLM Inference Scheduling

LLM inference workloads exhibit several properties that critically affect schedulability:

Each request requires a two-phase computation: a prefill phase (processing the prompt to initialize the KV cache) and a decode phase (autogenerating output tokens sequentially, each extending the KV cache) (Ao et al., 15 Apr 2025, Bari et al., 1 Aug 2025).
GPU memory consumption grows linearly with the number of generated tokens per request, rendering the classical notion of fixed-size jobs inapplicable and making online batching and eviction decisions sensitive to prediction errors in output length (Jaillet et al., 10 Feb 2025, Chen et al., 20 Aug 2025).
Memory overcommitment risks catastrophic out-of-memory (OOM) errors, while undercommitment leads to wasted resources and increased end-to-end latency.
Output lengths are often unknown at arrival time; state-of-the-art prediction techniques yield interval, binned, or relative ranking estimates rather than precise counts, further complicating scheduling (Zheng et al., 2023, Fu et al., 28 Aug 2024, Chen et al., 20 Aug 2025).

This suggests that the design of efficient schedules must explicitly incorporate memory growth, output uncertainty, prefill/decode phase transitions, and resource constraints into both the objective function and feasibility checks.

2. Predictive Scheduling under Output Length Uncertainty

A central problem in LLM inference scheduling is output length prediction and its integration into resource allocation:

Early systems employed First-Come-First-Serve (FCFS) scheduling, leading to Head-of-Line (HoL) blocking, where short requests are delayed by preceding longer requests, increasing queuing latency and reducing throughput (Fu et al., 28 Aug 2024, Choi et al., 14 May 2025).
Enhanced methods deploy lightweight predictors, ranging from classifier heads on LLMs to learning-to-rank models, to order requests by estimated completion or relative length (Zheng et al., 2023, Fu et al., 28 Aug 2024, Choi et al., 14 May 2025). For instance, sequence scheduling based on predicted maximal output lengths enables micro-batching requests with similar completion expectations, reducing padding and token wastage (Zheng et al., 2023).
Algorithms such as $\mathcal{A}_{\max}$ and $\mathcal{A}_{\min}$ (Chen et al., 20 Aug 2025) address the uncertainty explicitly: $\mathcal{A}_{\max}$ assumes the upper bound of the predicted interval for each request and avoids OOM at the cost of severe underutilization as prediction uncertainty increases, with a competitive ratio scaling as $\mathcal{O}(1/\alpha)$ for $\alpha = \ell/u$ (min/max predicted lengths). By contrast, $\mathcal{A}_{\min}$ initializes with the lower bound, greedily maximizes occupancy, then dynamically adjusts as actual token counts emerge, guaranteeing only $\mathcal{O}(\log(1/\alpha))$ loss—much more robust in practice and closer to hindsight-optimal scheduling.
Iterative and adaptive predictors, sometimes using encoder-based backbone models like BGE, incorporate partial outputs as additional context, improving refinement of remaining-inference-length estimates as generation progresses (Choi et al., 14 May 2025).

Interval-based, binned, and relative ranking predictors are now central to production LLM serving stacks, directly influencing micro-batch sizing, failure-triggered recomputation policies, and starvation prevention (Zheng et al., 2023, Chen et al., 20 Aug 2025).

3. Memory, Batch, and Resource Allocation Models

Modern LLM serving systems rely on precise memory modeling, fine-grained batching, and dynamic resource-aware scheduling.

Key-Value (KV) cache memory usage grows with each decoded token; thus, feasibility checks for batch formation must account for both prompt size $s_i$ and accumulated output tokens $a_i^{(t)}$ for each active job $i$ , imposing the constraint $\sum_{i\in\mathcal A^{(t)}} (s + a_i^{(t)}) \leq M$ , with $M$ denoting total GPU memory (Jaillet et al., 10 Feb 2025, Chen et al., 20 Aug 2025).
To minimize redundant computation and waiting caused by mixing jobs of disparate lengths, sequence scheduling and variable batch sizing techniques are adopted. For example, micro-batches are formed from requests whose predicted lengths fall into the same bin (cell size, e.g., 50 tokens); batch size is then scaled inversely with expected response length using $B = B_0 \cdot L / L_0$ (Zheng et al., 2023).
Failure-completion-and-recomputation (FCR) protocols detect if a response exceeds the predicted cap and reschedule it as a new job, with empirical studies finding a low (<20%) failure rate for bin- or interval-based classifiers (Zheng et al., 2023).
Hybrid cache schemes further expand effective batch size; approaches like Apt-Serve combine memory-intensive KV caching with lower-memory hidden state caching, effectively solving a hybrid knapsack problem at each batch selection (Gao et al., 10 Apr 2025).
In distributed deployments, memory- and power-aware frameworks dynamically place and migrate requests based on predicted memory growth (e.g., Llumnix’s “freeness” metric $F = (M - \sum V) / B$ ) or ensemble system constraints such as airflow, power budgets, and cooling limitations (TAPAS) (Sun et al., 5 Jun 2024, Stojkovic et al., 5 Jan 2025).

The synthesis of these models enables both proactive avoidance of resource contention and opportunistic expansion of throughput during memory slack.

4. Advanced Scheduling Algorithms and Theoretical Optima

The emergence of queueing theory and online scheduling has aligned LLM inference scheduling with rigorous optimization frameworks:

Throughput-optimality under heavy loading has been proven for “work-conserving” algorithms: any scheduler that fills iteration batches to token budget $b$ whenever feasible—mixing prefill and decode tokens as needed—achieves system stability whenever $\lambda(m_p + m_d) < b/t_b$ , with $m_p$ and $m_d$ being average prefill and decode token counts, and $t_b$ the batch time (Li et al., 10 Apr 2025, Bari et al., 1 Aug 2025).
Optimal Resource-Aware Dynamic (RAD) schedulers enforce “optimal tiling” for matrix multiplication on GPU, specified by forming batches of $t^*$ (least common multiple of preferred tile sizes) decode- or prefill-iterations, and dynamically switch between prefill- and decode-dominant scheduling based on the workload mix (Bari et al., 1 Aug 2025).
For practical tail-latency QoS (TBT, TTFT), SLO-Aware LLM Inference (SLAI) schedulers prioritize decode-iterations for requests close to missing per-token deadlines and reorder prefill-requests by prompt length, tuning batch formation using real-time memory and queue observations (Bari et al., 1 Aug 2025).
Fluid-guided online scheduling (WAIT and nested WAIT) algorithms set dynamic batch thresholds based on a continuous flow approximation (fluid model), yielding provable throughput approximations and bounded latency scaling in heavy traffic (Ao et al., 15 Apr 2025).
Speculative and semi-clairvoyant algorithms (e.g., LAPS-SD) accommodate additional uncertainty such as dynamic token acceptance rates (in speculative decoding): requests are scheduled using Least Attained Service (LAS) with priority queues until token acceptance stabilizes, then scheduled like SJF, yielding substantial latency reductions (Li et al., 20 May 2025).

These algorithmic advancements, including adaptation to noisy or partial forecasting, represent the currently established theoretical frontier in LLM inference scheduling.

5. Fairness, Locality, and Semantic Priority

Current research recognizes the importance of fair service and efficiency through hardware locality or semantic context:

Deficit Longest Prefix Match (DLPM) and Double Deficit LPM (D²LPM) algorithms guarantee fairness between clients while maintaining prefix locality, which increases cache reuse and throughput. Each client is awarded a deficit counter; requests with the longest prefixes are batched unless their deficit is low, ensuring no client is indefinitely starved. Distributed variants extend this principle with per-worker tokens and global load balancing (Cao et al., 24 Jan 2025).
Semantic scheduling leverages LLM-based semantic classifiers to annotate requests with urgency (e.g., using emergency severity indices in EMS scenarios), then combines this tag with estimated output cost in a min-heap scheduler. This is shown to dramatically reduce the waiting time for critical, time-sensitive requests—even achieving speedups of more than $6\times$ over SJF and $8\times$ over FCFS for high-urgency queries (Hua et al., 13 Jun 2025).
Stage-aware batching and dual-heap cache management ensure that high-priority or urgent requests are not blocked by batch formation or suffer eviction of critical KV caches, tying together content-aware and resource-optimal strategies (Hua et al., 13 Jun 2025).
Starvation prevention (e.g., by advancing the priority of requests with high starvation counters) is implemented both in ranking-based and strict SJF-like schedulers to avoid unhealthy service inequities (Fu et al., 28 Aug 2024, Choi et al., 14 May 2025).

This suggests that practical deployments must account for both system-level fairness and hardware/effective cache utilization, often requiring compromise between strict QoS and maximal efficiency.

6. Distributed and Multi-Stage Deployment Considerations

Modern LLM inference is deployed across distributed, heterogeneous, and sometimes multi-tenant GPU clusters:

Distributed schedulers like ExeGPT optimize resource allocation at both layer/block level and hardware partitioning granularity, using round-robin or workload-aware allocation and branch-and-bound search to balance throughput with strict latency constraints (Oh et al., 15 Mar 2024).
Edge-cloud collaborative architectures (PerLLM) incorporate combinatorial multi-armed bandit optimization with constraint satisfaction; assignments are selected via augmented UCB, taking into account per-job QoS, current server/bandwidth states, and energy cost, leading to both improved throughput and dramatic reductions in energy (Yang et al., 23 May 2024).
Hierarchical scheduling for agentic, multi-stage workflows (HEXGEN-TEXT2SQL) employs global workload-balanced dispatch and local urgency-guided prioritization, using simulation-based hyperparameter tuning to minimize end-to-end latency and SLO violations under dependency constraints (Peng et al., 8 May 2025).
Integrated serving and training (LeMix) fuses offline profiling, per-task execution prediction, and memory-aware runtime scheduling to permit simultaneous, efficient co-location of inference and retraining. This achieves $3.53\times$ throughput improvements and $2.12\times$ higher SLO attainment, exploiting idleness-aware pipelining and quality-aware dispatch (Li et al., 28 Jul 2025).
Scheduling frameworks are increasingly required to adapt not only to model and dataset heterogeneity but also to environmental factors such as thermal, power, and cooling constraints, using predictive models to guide both placement and dynamic reconfiguration (TAPAS) (Stojkovic et al., 5 Jan 2025).

The deployment context thus shapes the choice and granularity of scheduling decisions, from within-iteration batch formation to cross-node resource allocation and real-time adaptation.

7. Practical Implications and Future Research Directions

Recent studies demonstrate substantial practical impact:

Empirical results show that properly engineered scheduling pipelines can double effective throughput, reduce TTFT by over 50%, and dramatically increase service capacity while maintaining tail latency SLOs (Bari et al., 1 Aug 2025, Gao et al., 10 Apr 2025).
Energy savings, robustness to noisy predictions, and ability to maintain degraded performance under extreme workloads (e.g., variable output length, unpredictable arrival patterns) are now recognized as core requirements, with adaptively robust scheduling (as in $\mathcal{A}_{\min}$ ) offering guarantees even under adversarial input (Chen et al., 20 Aug 2025).
Interdisciplinary integration of online scheduling theory, queuing analysis, memory-efficient caching, and LLM-specific behavioral profiling is needed to further improve both analytical guarantees and real-world robustness (Li et al., 10 Apr 2025, Ao et al., 15 Apr 2025, Li et al., 28 Jul 2025).
Open challenges remain in handling multi-stage agentic workflows, semantic quality-of-service prioritization, speculative decoding uncertainty, distributed fairness, and real-time autoscaling in cloud platforms.

LLM inference scheduling has evolved into a mathematically grounded, highly optimized research area with direct impact on production-scale deployments. Research continues to refine these frameworks to address emerging forms of LLM workloads, novel forms of heterogeneity, and increasing user expectations for both efficiency and fairness.