Adaptively Robust LLM Inference Optimization

• Adaptively Robust LLM Inference Optimization is a set of techniques that use predictive interval outputs and dynamic scheduling to efficiently manage resource usage during LLM inference.
• The approach leverages algorithms like Aₘₐₓ for conservative scheduling and Aₘᵢₙ for adaptive, near-optimal performance by emphasizing lower-bound predictions.
• Empirical evaluations and theoretical guarantees show that adaptive scheduling reduces latency and energy consumption while maximizing throughput in heterogeneous, real-time environments.

Adaptively robust LLM inference optimization refers to the class of system-level and algorithmic techniques that dynamically reduce inference latency, throughput constraints, memory bottlenecks, and energy overhead under real-time uncertainty, heterogeneous hardware, and unpredictable workloads. These methods combine predictive machine learning, dynamic scheduling, rigorous memory management, and online adaptation to overcome challenges arising from sequential auto-regressive generation, fluctuating prompt loads, and the resource-intensive nature of modern transformers. The field leverages both theoretical algorithmic guarantees and empirical system engineering to enable production-grade, high-volume LLM serving under strict reliability, cost, and efficiency constraints.

1. Scheduling Algorithms under Prediction Uncertainty

A central bottleneck in LLM inference arises from the need to allocate memory and schedule requests without knowing output sequence lengths in advance—the output length (number of generated tokens) is not available at admission time and can dramatically affect overall resource usage. To address this, adaptively robust schedulers employ interval classification, wherein a prediction model provides a lower and upper bound $[\ell, u]$ for each request’s output length.

Two principal algorithms are proposed in (Chen et al., 20 Aug 2025):

• $\mathcal{A}_{\max}$ (Conservative Scheduling): Schedules active requests as if each will require its maximum predicted output. Thus, each job is admitted assuming its cost is $s + u$ (where $s$ is prompt length, $u$ is the interval upper bound). This ensures that the memory constraint (e.g., for key/value cache) is never violated, but tends to be overly conservative, severely limiting batch concurrency when $u$ is much larger than actual $o_i$.
• $\mathcal{A}_{\min}$ (Adaptive Scheduling): Schedules requests optimistically at the lower bound $s + \ell$, but then dynamically tracks actual output as tokens are generated. If a job threatens to overflow memory, the algorithm greedily evicts the request(s) with the smallest current $v_o$ until constraints are respected.

Theoretical Guarantees

• $\mathcal{A}_{\max}$ has a competitive ratio upper bounded by $$\mathrm{CR}(\mathcal{A}_{\max}) \leq \frac{1}{2}\left(\alpha^{-1}(1 + \alpha^{-1})\right) + \mathcal{O}(1/M)$$, with $\alpha = \ell/u$. As prediction intervals widen ($\alpha \rightarrow 0$), scheduler efficiency degrades sharply.
• $\mathcal{A}_{\min}$ achieves an $\mathcal{O}(\log(\alpha^{-1}))$ competitive ratio: as uncertainty increases, the additional latency increases only logarithmically.

In practice, $\mathcal{A}_{\min}$ achieves latency comparable to an ideal hindsight scheduler ("H-SF") that knows true output lengths, even under extremely uncertain interval predictions.

Empirical Evaluation

Three simulation regimes are demonstrated:

Scenario	$\mathcal{A}_{\max}$	$\mathcal{A}_{\min}$
All intervals very wide (e.g., $[1,1000]$)	High latency, low batch concurrency	Near-optimal latency, matches hindsight
Grouped intervals by classification (e.g., $[1,100]$, etc.)	Improved latency, but still suboptimal	Robust, near-optimal across classes
Intervals $[(1-x)o_i, (1+x)o_i]$ for each request	Latency degrades rapidly as $x$ increases	Robust; latency near that of hindsight

The key advantage is that $\mathcal{A}_{\min}$ is not sensitive to upper-bound prediction errors, relying only on the more reliable lower bound, which is typically easier to learn or classify via machine learning.

2. Online Adaptation and Memory-Constrained Batching

Under the memory limit $M$ (often determined by available high-bandwidth cache for key/value tensors across transformer layers), both algorithms maintain an invariant such as

$\sum_{i\in\mathcal{A}^{(t)}}(s + a_i^{(t)}) \leq M$

where $\mathcal{A}^{(t)}$ is the set of active jobs, and $a_i^{(t)}$ the tokens generated so far by job $i$. In $\mathcal{A}_{\min}$, as jobs overrun their lower bound, the scheduler dynamically updates $v_o$ for each, evicting requests with smallest memory usage when aggregate cost approaches $M$.

This dynamic, online update is critical to adaptively maximize batching; aggressive initial scheduling increases system utilization, while eviction limits the risk of overflow as partial trajectories reveal actual requirements.

3. Handling Prediction Uncertainty via Interval Classification

The design leverages interval-classification predictors for output length, a practical departure from classical point estimation. This design makes no assumptions about the distribution of output lengths, and accommodates ML-based predictors with interval outputs—either group-based (binning jobs into classes) or instance-based (per-request interval estimation).

Theoretically, under only knowing $[\ell, u]$ for each request, the adaptive scheduler’s resilience is established: The worst-case additive cost is logarithmic in $\alpha^{-1}$, implying strong performance robustness against distributional uncertainty or model misspecification.

A practical implication is that ML classifiers predicting even coarse intervals can enable near-optimal scheduling when paired with an adaptive batching policy. Prediction of the upper bound, being more error-prone, is de-emphasized.

4. Implications for System Efficiency and Energy Usage

By substantially improving throughput and reducing average per-job latency compared to conservative methods, adaptive scheduling also lowers overall energy consumption. Because energy usage in LLM inference is dominated by time active and parallel resource utilization, the ability to maximize the number of concurrent jobs directly ameliorates the "energy-delay product" and related cost metrics.

The adaptively robust design ensures no memory overflow (no job canceled or recomputed), preserves tail latency by efficiently serving both short and long requests, and is compatible with ML serving workloads with high variability.

5. Integration with Broader LLM Inference Ecosystem

The methods detailed in (Chen et al., 20 Aug 2025) are complementary to algorithmic and systems-level advances surveyed in (Zhen et al., 28 Apr 2025) and (Pan et al., 27 Jun 2025), which emphasize dynamic batching, paged KV-cache allocation, predictive scheduling, and asynchronous memory management as key strategies for robust high-throughput LLM serving. Adaptive scheduling under uncertainty can be further combined with memory disaggregation, hybrid cloud-edge deployment (Chen et al., 3 Jun 2024), and energy-optimized operation (Ye et al., 3 Aug 2025) to form end-to-end robust LLM inference systems.

6. Future Directions and Research Opportunities

• Incorporation of full output-length distributions: Extending from interval-based predictions to full probabilistic modeling of output length could further improve scheduler decisions, optimally weighting risk across the distribution.
• Policy switching architectures: Combining multiple scheduling policies (e.g., adaptive, conservative, mixture-specific) with real-time monitoring of prediction accuracy or resource contention.
• Feedback-driven prediction adjustment: Systems can integrate online feedback from observed output lengths to update and recalibrate predictors and scheduling thresholds.
• Extension to distributed and multi-worker settings: Adjusting adaptive batching and eviction to coordinate over distributed memory resources and worker nodes.
• Joint latency-energy optimization: Future algorithms could explicitly incorporate energy cost alongside latency and resource utilization in competitive ratio and scheduler design.

7. Summary Table: Algorithmic Properties

Algorithm	Uses Lower Bound	Uses Upper Bound	Batch Size Efficiency	Risk of Overflow	Asymptotic Competitive Ratio
$\mathcal{A}_{\max}$	No	Yes	Low (conservative)	None	$\mathcal{O}(\alpha^{-1})$
$\mathcal{A}_{\min}$	Yes	No	High (aggressive, dynamic)	None	$\mathcal{O}(\log(\alpha^{-1}))$

This characterization encapsulates the trade-off: $\mathcal{A}_{\min}$ achieves robust, adaptive performance (low latency, high throughput, controlled risk) by exploiting more reliable lower bound predictions and online update, thereby meeting practical demands of large-scale LLM inference under significant output-length uncertainty.