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arxiv: 2407.12391 · v1 · pith:7X2XVFCQnew · submitted 2024-07-17 · 💻 cs.DC · cs.AI

LLM Inference Serving: Survey of Recent Advances and Opportunities

classification 💻 cs.DC cs.AI
keywords surveyrecentservingabreastadvancementsadvancesalteringcomprehensive
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This survey offers a comprehensive overview of recent advancements in Large Language Model (LLM) serving systems, focusing on research since the year 2023. We specifically examine system-level enhancements that improve performance and efficiency without altering the core LLM decoding mechanisms. By selecting and reviewing high-quality papers from prestigious ML and system venues, we highlight key innovations and practical considerations for deploying and scaling LLMs in real-world production environments. This survey serves as a valuable resource for LLM practitioners seeking to stay abreast of the latest developments in this rapidly evolving field.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. When Is the Same Model Not the Same Service? A Measurement Study of Hosted Open-Weight LLM APIs

    cs.PF 2026-05 conditional novelty 7.0

    Hosted open-weight LLMs function as heterogeneous, time-varying services rather than uniform model artifacts, with concentrated demand, decoupled supply and adoption, and measurable gains from task-aware routing.

  2. When Is the Same Model Not the Same Service? A Measurement Study of Hosted Open-Weight LLM APIs

    cs.PF 2026-05 unverdicted novelty 7.0

    Hosted open-weight LLM APIs function as time-varying heterogeneous services rather than fixed model artifacts, with demand concentrated, supply-use mismatches, and task-specific routing yielding major cost and through...

  3. Sutradhara: An Intelligent Orchestrator-Engine Co-design for Tool-based Agentic Inference

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    Sutradhara co-designs orchestrator and LLM serving to overlap tool execution with prefill, stream tool dispatch during decode, and use semantic hints for cache management, yielding up to 77% higher load at fixed media...

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    RAP is a reinforcement learning framework for runtime-adaptive pruning of LLMs that jointly optimizes model weights and KV-cache usage under varying memory budgets.

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    Workload-aware optimizations for LLM serving in AML and fraud detection yield substantial gains in throughput, latency, and GPU utilization on synthetic compliance prompts.

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    A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.