arXiv AI
Jun 16
Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving
arXiv:2512. 22420v5 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates LLM inference by verifying draft tokens in parallel.
MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Model Serving
MAPS is a Memory-Aware Predictive Scheduling framework designed for disaggregated large language model (LLM) serving. It uses device-assisted speculative output length prediction and uncertainty-aware calibration to establish safe output-length upper bounds, which inform a hierarchical global-local scheduling strategy that reduces queue buildup and head-of-line blocking. Experiments on real-world workloads and two LLMs demonstrate that MAPS lowers average end-to-end latency by 42.6% and tail latency by up to 84.8% compared to three state-of-the-art systems.
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Beyond Prediction: Tail-Aware Scheduling for LLM Inference
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MemSpec: Memory-Aware Runtime for Adaptive Draft Scheduling in Speculative Decoding on Edge Devices
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LLM Serving Optimization with Variable Prefill and Decode Lengths
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Jul 7
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
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SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
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OUTLETS: Output-Length Prediction from Speculative Decoding Backbones
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arXiv AI
Sep 16
End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services
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arXiv AI
Sep 10
A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware
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arXiv AI
Jul 23
FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads
arXiv:2607. 19349v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge.
arXiv Machine Learning
Aug 10
Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving
arXiv:2608. 06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests.
arXiv AI
Jul 7
Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving
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