Latency-Aware Inference Optimization

• Latency-aware inference is a methodology that models wall-clock latency as a function of hardware memory, compute constraints, and system load.
• It integrates analytic predictors, DVFS models, and dynamic scheduling to balance accuracy and throughput under strict service level objectives.
• The approach guides design in multi-tier, edge/cloud, and multi-accelerator systems to achieve provable improvements in real-time responsiveness.

Latency-aware inference is the set of methodologies, frameworks, and decision models that explicitly optimize neural network or LLM inference for minimal wall-clock response time, taking into account platform-specific memory and processor bottlenecks, runtime concurrency, hardware scheduling, and practical compute constraints. Unlike compute-optimal approaches focused on theoretical FLOPs or generated tokens, latency-aware inference aligns algorithmic design, scheduling, and resource management to achieve provable improvements in real-time responsiveness—often under strict Service Level Objectives (SLOs)—without compromising accuracy or resource budgets.

1. Foundational Principles and Latency Modeling

Latency-aware inference distinguishes itself by rejecting surrogate metrics such as FLOPs, token count, or static operation counts, and instead models latency as a function of hardware characteristics, memory management, data movement, scheduling overhead, and dynamic system load.

• GPU Memory-bound Regimes: In autoregressive decoding for LLMs, wall-clock latency per token generation is memory-bound rather than compute-bound. Fetching large weights and key-value caches from high-bandwidth memory (HBM) dictates the critical path rather than arithmetic operations (Wang et al., 26 May 2025).
• Analytic and Regression Models: Modern frameworks use analytic latency predictors that aggregate components such as data transfer time, kernel launch overhead, tiling strategies, and operator fusion (see formulas for $L = L_\text{data} + L_\text{comp}$ in (Han et al., 2022, Han et al., 2023, Han et al., 10 Feb 2025)).
• DVFS-aware Models: Dynamic Voltage and Frequency Scaling (DVFS) yields a latency function $t_n(f) = a_n f^{-b_n} + c_n$, where $a_n$ models workload size, $b_n$ frequency sensitivity, and $c_n$ constant kernel/memory delays. This captures both compute-bound and memory-saturation regimes (Han et al., 10 Feb 2025).

2. Latency-Aware Test-Time Scaling in LLMs

Test-Time Scaling (TTS) methods improve LLM inference by dynamically adjusting the number and structure of candidate responses, with latency-optimal strategies diverging sharply from compute-optimal scaling.

• Sequential vs. Parallel Scaling: Sequential scaling—with long chains-of-thought per pass—maximizes accuracy per token but suffers from extremely low throughput due to repeated weight loads. Parallel scaling—multiple short branches—offers much higher throughput and, therefore, superior wall-clock latency despite lower per-token efficiency (Wang et al., 26 May 2025).
• Branch-wise and Sequence-wise Parallelism: Allocating resources to concurrent inference branches (branch-wise) or speculative decoding of multiple candidate sequences (sequence-wise) enables substantial latency reduction. Empirically, a 32B model achieved 82.3% accuracy on MATH-500 in 1 minute via these configurations (Wang et al., 26 May 2025).
• Joint Utility Optimization: Formalize strategy selection per query via $$U_s(x) = a_s(x) - \lambda_T T_s(x) - \lambda_L L_s(x)$$, where $a_s(x)$ is predicted accuracy, $T_s(x)$ the token cost, and $L_s(x)$ the measured latency. Query routing via lightweight predictors and precomputed latency tables delivers superior accuracy-latency trade-offs (Huang et al., 11 Sep 2025).

3. Adaptive Resource Allocation and Scheduling Frameworks

Latency-aware inference extends to multi-query systems and shared resource environments via dynamic scheduling and placement algorithms.

• Service-Aware Scheduling: SeaLLM optimizes latency by computing normalized latency per request, $$L_n = \sum_{s \in S} \sum_{r \in R_s} (L_r / \hat L_s) / \sum_s |R_s|$$, and serving requests based on doubly-prioritized budget queues, preempted at token-level granularity (Zhao et al., 22 Apr 2025).
• Placement and Replacement Strategies: Adaptive partitioning of GPUs into tensor-parallel groups, followed by dynamic assignment of services, minimizes cluster-wide latency subject to SLO constraints. An adaptive replacement interval tuned by observed performance error keeps system response aligned with workload drift (Zhao et al., 22 Apr 2025).
• Unified Memory Management: Sharing GPU memory among services with a merged-block key-value cache reduces prefill and per-token decode latency by improving address locality and minimizing context-switch overhead (Zhao et al., 22 Apr 2025).

4. Latency-Aware Design in Dynamic and Multi-Accelerator Networks

Latency-aware inference in dynamic neural networks and heterogeneous SoCs couples mask-generation algorithms with hardware-informed scheduling.

• Unified Dynamic Network Design: Frameworks like LAUDNet and LASNet synthesize spatial, channel, and layer-level adaptivity in a single block, using analytic latency predictors to guide granularity selection (patch size, channel group size, mask activation rates) and operator fusion for maximal practical speedup (Han et al., 2023, Han et al., 2022).
• ODiMO Mapping: Fine-grained, channel-wise discrete assignment of layer channels to accelerators, subject to per-layer latency models, builds Pareto-optimal frontiers for accuracy vs. latency across heterogeneous engines. Realizing up to 31% latency reduction at sub-percent accuracy cost (Risso et al., 2023).
• Sparse and Low-latency FPGA Operators: PolyLUT replaces conventional neurons with single-cycle LUT polynomial logic, leveraging structured pruning to restrict fan-in and maximize resource utilization, achieving up to 18.3× speedup vs. classic BNNs (Andronic et al., 14 Jan 2025).

5. Edge, Cloud, and Multi-Tier Latency Optimizations

Real-world deployment of latency-aware inference involves per-query routing, tailored partitioning, and latency-centric autoscaling.

• Edge-Latency Routing: Per-query device assignment, informed by empirical batch-wise latency tables, enables edge clusters to halve end-to-end latency with only moderate carbon budget increases. Batch size four is identified as the optimal trade-off point between throughput and energy (Rajashekar et al., 1 Nov 2025).
• Predictive Routing and Autoscaling: LA-IMR deploys a closed-form affine power-law latency model to execute millisecond-scale offloading and proactive replica autoscaling—ahead of queue buildup—yielding up to 20.7% reduction in P99 tail latency in cloud robotics (Seo et al., 12 May 2025).
• Multi-Turn LLM Routing in Wireless Settings: Dynamic Quality–Latency Aware Routing fuses semantic difficulty prediction with latency cost models (including KV-cache management overheads) to halve LLM invocations and cut latency by 5–15% for typical conversational workloads (Bao et al., 15 Aug 2025).

6. Measurement, Prediction, and Practical Deployment Guidelines

• Latency Prediction for NAS/Edge: Operation-wise latency predictors (GBDT, RF, MLP) trained on synthetic and real architectures achieve sub-10% mean absolute percentage error (MAPE) across diverse mobile hardware and kernel scenarios. Accounting for framework-level fusion and kernel selection is essential (Li et al., 2022).
• Analytic Models for Scheduling: When designing for latency, coarse granularity and operator fusion restore memory contiguity, facilitating practical speedup. Fine-grained pixel-level or ungrouped channel sparsity can negate benefits due to kernel launch and memory access overhead (Han et al., 2022, Han et al., 2023).

7. State-of-the-Art Results and Impact

Latency-aware inference advances have produced quantifiable gains across domains:

Scenario	Model/System	Latency Reduction	Accuracy Cost	Hardware
LLM Reasoning	Branch+Speculative	up to 5×	<2 pp	GPU
Dynamic ConvNets	LAUDNet/LASNet	36–53% (ResNet101)	<1 pp	V100/TX2 GPUs
Multi-Accelerators	ODiMO	up to 31%	<0.5 pp	DIANA SoC
FPGA	PolyLUT	up to 18×	<2 pp	xcvu9p
Edge LLM	Latency-Aware Routing	2–3×	modest carbon↑	Jetson/Ada
Cloud Robotics	LA-IMR	up to 20.7% P99	n/a	ARM x86 CPUs
Wireless LLM	Quality-Latency Route	5–15%	none	Mobile+Edge

Further advances will combine analytic latency models with learning-based predictors, joint energy-latency trade-offs, and hardware-software co-design, spanning multi-tier and edge deployment scenarios (Han et al., 10 Feb 2025, Rajashekar et al., 1 Nov 2025, Wang et al., 26 May 2025, Risso et al., 2023, Zhao et al., 22 Apr 2025, Han et al., 2022, Han et al., 2023).

• "Faster and Better LLMs via Latency-Aware Test-Time Scaling" (Wang et al., 26 May 2025)
• "Latency and Token-Aware Test-Time Compute" (Huang et al., 11 Sep 2025)
• "SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for LLM Inference" (Zhao et al., 22 Apr 2025)
• "Dynamic Network Adaptation at Inference" (Mendoza et al., 2022)
• "DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis" (Han et al., 10 Feb 2025)
• "Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference" (Risso et al., 2023)
• "PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning" (Andronic et al., 14 Jan 2025)
• "Latency-aware Spatial-wise Dynamic Networks" (Han et al., 2022)
• "Latency-aware Unified Dynamic Networks for Efficient Image Recognition" (Han et al., 2023)
• "Inference Latency Prediction at the Edge" (Li et al., 2022)
• "LA-IMR: Latency-Aware, Predictive In-Memory Routing and Proactive Autoscaling for Tail-Latency-Sensitive Cloud Robotics" (Seo et al., 12 May 2025)
• "Toward Sustainability-Aware LLM Inference on Edge Clusters" (Rajashekar et al., 1 Nov 2025)
• "Dynamic Quality-Latency Aware Routing for LLM Inference in Wireless Edge-Device Networks" (Bao et al., 15 Aug 2025)
• "LLM Partitioning for Low-Latency Inference at the Edge" (Kafetzis et al., 5 May 2025)