# AdaSpec: Adaptive Speculative Decoding for Fast, SLO-Aware Large Language Model Serving

arXiv: https://arxiv.org/abs/2503.05096 · HTML: https://mlsanity.com/paper/2503.05096/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving.
- Builds on 24 of its 44 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Placed in

- Efficient LLM inference and serving / [Speculative decoding](https://mlsanity.com/inference/speculative-decoding/)

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [Splitwise: Efficient generative LLM inference using phase splitting](https://mlsanity.com/paper/2311.18677/) (2023)
- [Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve](https://mlsanity.com/paper/2403.02310/) (2024)
- [EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://mlsanity.com/paper/2406.16858/) (2024)
- [SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification](https://mlsanity.com/paper/2305.09781/) (2023)
- [Break the Sequential Dependency of LLM Inference Using Lookahead Decoding](https://mlsanity.com/paper/2402.02057/) (2024)
- [Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding](https://mlsanity.com/paper/2401.07851/) (2024)
- [Speculative Decoding with Big Little Decoder](https://mlsanity.com/paper/2302.07863/) (2023)
- [REST: Retrieval-Based Speculative Decoding](https://mlsanity.com/paper/2311.08252/) (2023)
- [TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding](https://mlsanity.com/paper/2404.11912/) (2024)
- [BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems](https://mlsanity.com/paper/2401.17644/) (2024)
- [GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding](https://mlsanity.com/paper/2402.02082/) (2024)
- [LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages](https://mlsanity.com/paper/2407.05975/) (2024)
- [Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services](https://mlsanity.com/paper/2404.16283/) (2024)
- [Queue management for slo-oriented large language model serving](https://mlsanity.com/paper/2407.00047/) (2024)

## Built on by (most cited first, 18 of 18)

- [SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving](https://mlsanity.com/paper/2506.09397/) (2025)
- [Utility-Driven Speculative Decoding for Mixture-of-Experts](https://mlsanity.com/paper/2506.20675/) (2025)
- [ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios](https://mlsanity.com/paper/2604.09603/) (2026)
- [Beat the long tail: Distribution-Aware Speculative Decoding for RL Training](https://mlsanity.com/paper/2511.13841/) (2025)
- [A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness](https://mlsanity.com/paper/2510.13890/) (2025)
- [SpecRouter: Adaptive Routing for Multi-Level Speculative Decoding in Large Language Models](https://mlsanity.com/paper/2505.07680/) (2025)
- [TIDE: Temporal Incremental Draft Engine for Self-Improving LLM Inference](https://mlsanity.com/paper/2602.05145/) (2026)
- [MineDraft: A Framework for Batch Parallel Speculative Decoding](https://mlsanity.com/paper/2603.18016/) (2026)
- [FASER: Fine-Grained Phase Management for Speculative Decoding in Dynamic LLM Serving](https://mlsanity.com/paper/2604.20503/) (2026)
- [Adversarial Prompts for Acceptance Collapse in Speculative Decoding](https://mlsanity.com/paper/2607.21804/) (2026)
- [AgentSpec: Speculative Decoding for Batch Inference of LLM Agents](https://mlsanity.com/paper/2608.24004/) (2026)
- [TreeSpark: Calibrated, Load-Adaptive Draft Trees for Semi-Autoregressive Speculative Decoding](https://mlsanity.com/paper/2609.22098/) (2026)
- [Crossflow: Prefill-Decode Elasticity for Agentic LLM Serving](https://mlsanity.com/paper/2609.27085/) (2026)
- [Regulating Branch Parallelism in LLM Serving](https://mlsanity.com/paper/2605.06914/) (2026)
- [Hybrid Verified Decoding: Learning to Allocate Verification in Speculative Decoding](https://mlsanity.com/paper/2606.01019/) (2026)
- [CONCORD: Asynchronous Sparse Aggregation for Device-Cloud RAG under Document Isolation](https://mlsanity.com/paper/2606.15179/) (2026)
- [Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection](https://mlsanity.com/paper/2607.05148/) (2026)
- [OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving](https://mlsanity.com/paper/2609.14237/) (2026)

## Abstract (arXiv)

Cloud-based Large Language Model (LLM) services often face challenges in achieving low inference latency and meeting Service Level Objectives (SLOs) under dynamic request patterns. Speculative decoding, which exploits lightweight models for drafting and LLMs for verification, has emerged as a compelling technique to accelerate LLM inference. However, existing speculative decoding solutions often fail to adapt to fluctuating workloads and dynamic system environments, resulting in impaired performance and SLO violations. In this paper, we introduce AdaSpec, an efficient LLM inference system that dynamically adjusts speculative strategies according to real-time request loads and system configurations. AdaSpec proposes a theoretical model to analyze and predict the efficiency of speculative strategies across diverse scenarios. Additionally, it implements intelligent drafting and verification algorithms to maximize performance while ensuring high SLO attainment. Experimental results on real-world LLM service traces demonstrate that AdaSpec consistently meets SLOs and achieves substantial performance improvements, delivering up to 66% speedup compared to state-of-the-art speculative inference systems. The source code is publicly available at https://github.com/cerebellumking/AdaSpec