Jul 2026· ACM SIGOPS Operating Systems Review· Vol 60, pp. 50 - 59· 0 citations· 48 references
TL;DR
MaverIQ is an intent-based LLM inference serving system that automatically maps user intents to deployment configurations while minimizing operational cost for the provider and reduces profiling overheads by 7-15× compared to state-of-the-art baselines.
Abstract
Large Language Models (LLMs) expose a large deployment configuration space spanning parallelism and compression techniques, with each configuration introducing different tradeoffs in latency, memory consumption, cost, and output quality. Existing systems either rely on expensive profiling across deployment configurations or inefficiently utilize fragmented GPU resources in multi-tenant clusters. We present MaverIQ, an intent-based LLM inference serving system that automatically maps user intents to deployment configurations while minimizing operational cost for the provider. To reduce profiling overheads, MaverIQ introduces lightweight LLM fingerprints and analytical models that extrapolate latency and memory footprint from only a few observations. To efficiently utilize fragmented GPU resources, MaverIQ leverages our observation that, unlike training, unevenly distributing LLM layers across GPUs has little impact on inference latency. Our evaluation shows that MaverIQ reduces profiling cost by 7-15× compared to state-of-the-art baselines and reduces operational cost by 3.8-8.3× across diverse LLMs, traces, and loads while effectively meeting user intents. Our code is available at https://github.com/UT-SysML/MaverIQ.
EPIC mitigates imbalance via performance-aware expert migration and runtime expert activation, and then improves communication with topology-adaptive transport kernels and fine-grained computation-communication overlap.
Jiamin Cao, Qingxu Li, Yaozhong Liu et al.· Proceedings of the ACM SIGCO...· 0 citations
Or OrionInfer, an adaptive LLM serving system that aligns inference strategies with real-time demand and introduces three key techniques: runtime switching between data parallelism and tensor parallelism with negligible overhead, an efficient inference pipeline that preserves batching efficiency during parallelism transitions, and live-migration-based load balancing to alleviate memory pressure and improve resource utilization.
Jingqi Feng, Guang Yang, Yukai Huang et al.· Proceedings of the 32nd ACM...· 0 citations
SLIM (Saturation-Aware Lightweight Performance Model), a semi-analytical model that predicts LLM inference throughput and latency from analytical formulations of Transformer computation and memory traffic, is introduced, which outperforms representative performance-modeling baselines while successfully generalizing to previously unseen operating conditions.
Pol G.Recasens, F. Agulló, Yue Zhu et al.· 0 citations
Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a proxy for the deployment behavior that matters in language-model inference. We identify a benchmark-to-deployment gap: candidate kernels that appear correct and fast in standalone harnesses can exhibit different performance, safety, or phase behavior after integration into a real inference workload. We introduce LLM4LLM, a deployment-aware closed-loop optimization framework that starts from a target inference script, extracts phase-aware optimization tasks, searches with an experience-guided episodic agent, and accepts patches through in-model validation. Across ten language-model inference workloads on A100 and H100 GPUs, LLM4LLM improves end-to-end latency for every evaluated model, achieving 3.91$\times$/6.98$\times$ geometric-mean speedups on A100/H100; as supporting kernel-level evidence, it also attains up to 2.745$\times$ GeoMean speedup on KernelBench Level 2.
Hui Zeng, Pengfei Yang, Yanxin Chen et al.· 0 citations
Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resource-constrained edge and mobile environments. In such settings, energy consumption, execution time, and memory usage directly affect practical usability, yet existing evaluations of LLM efficiency largely rely on proxy descriptors such as parameter count or FLOPs, often decoupled from task precision. This paper introduces a PTME-based experimental framework for the precision-aware profiling of lightweight LLM inference, jointly measuring Precision, execution Time, peak Memory usage, and Energy consumption through direct hardware-level measurements. The methodology is applied to a representative set of lightweight LLMs executed locally under edge-class resource envelopes on a controlled desktop platform, using benchmarks spanning code generation, mathematical reasoning, and multi-task understanding. We find that static proxy descriptors approximate inference cost well but fail to predict precision. Tightening the resource envelope increases cost without affecting precision, amplifying execution time more strongly than energy and penalizing larger models the most. Moreover, no single model dominates across all PTME dimensions, and a Pareto analysis reveals non-dominated configurations that would be hidden by accuracy-only or efficiency-only assessments, providing practical guidance for selecting models under different resource envelopes. These results show that selecting lightweight LLMs by size, FLOPs, latency, or accuracy alone can select the wrong deployment candidate; PTME profiling exposes configurations that preserve useful accuracy at lower physical cost.
Tomohiro Harada, Enrique Alba, Gabriel Luque· 0 citations
A structured, paired comparison of configuration complexity and performance across six platforms (Proxmox VM and LXC, native KVM, OpenStack VM and Zun, and Podman) on a single NVIDIA RTX 4500 Ada GPU, emphasizing the under-documented container paths
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