# Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy

arXiv: https://arxiv.org/abs/2312.12728 · HTML: https://mlsanity.com/paper/2312.12728/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in Serving systems and request scheduling, one of 12 directions in Efficient LLM inference and serving.
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Placed in

- Efficient LLM inference and serving / [Serving systems and request scheduling](https://mlsanity.com/inference/serving-systems-and-request-scheduling/)

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification](https://mlsanity.com/paper/2305.09781/) (2023)
- [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://mlsanity.com/paper/2004.12993/) (2020)
- [Blockwise Parallel Decoding for Deep Autoregressive Models](https://mlsanity.com/paper/1811.03115/) (2018)
- [Block Pruning For Faster Transformers](https://mlsanity.com/paper/2109.04838/) (2021)
- [Accelerating Transformer Inference for Translation via Parallel Decoding](https://mlsanity.com/paper/2305.10427/) (2023)
- [Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation](https://mlsanity.com/paper/2006.10369/) (2020)
- [Non-Autoregressive Machine Translation with Latent Alignments](https://mlsanity.com/paper/2004.07437/) (2020)
- [A Tensorized Transformer for Language Modeling](https://mlsanity.com/paper/1906.09777/) (2019)
- [Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding](https://mlsanity.com/paper/2310.05424/) (2023)
- [PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance](https://mlsanity.com/paper/2206.12562/) (2022)
- [Inference with Reference: Lossless Acceleration of Large Language Models](https://mlsanity.com/paper/2304.04487/) (2023)
- [Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision](https://mlsanity.com/paper/2110.07515/) (2021)
- [Accelerating BERT Inference for Sequence Labeling via Early-Exit](https://mlsanity.com/paper/2105.13878/) (2021)
- [STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition](https://mlsanity.com/paper/2302.01172/) (2023)
- [Lossless Acceleration for Seq2seq Generation with Aggressive Decoding](https://mlsanity.com/paper/2205.10350/) (2022)

## Built on by (most cited first, 30 of 37)

- [SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning](https://mlsanity.com/paper/2504.07891/) (2025)
- [KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference](https://mlsanity.com/paper/2502.04420/) (2025)
- [DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference](https://mlsanity.com/paper/2404.00242/) (2024)
- [Continuous Speculative Decoding for Autoregressive Image Generation](https://mlsanity.com/paper/2411.11925/) (2024)
- [Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning](https://mlsanity.com/paper/2510.19338/) (2025)
- [Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact Match](https://mlsanity.com/paper/2511.22972/) (2025)
- [Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding](https://mlsanity.com/paper/2503.10135/) (2025)
- [When Speculation Spills Secrets: Side Channels via Speculative Decoding In LLMs](https://mlsanity.com/paper/2411.01076/) (2024)
- [Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding](https://mlsanity.com/paper/2411.13157/) (2024)
- [FBS: Modeling Native Parallel Reading inside a Transformer](https://mlsanity.com/paper/2601.21708/) (2026)
- [SmallKV: Small Model Assisted Compensation of KV Cache Compression for Efficient LLM Inference](https://mlsanity.com/paper/2508.02751/) (2025)
- [Speculative Decoding with a Speculative Vocabulary](https://mlsanity.com/paper/2602.13836/) (2026)
- [Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding](https://mlsanity.com/paper/2605.20104/) (2026)
- [A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models](https://mlsanity.com/paper/2405.13019/) (2024)
- [Scaling Laws for Speculative Decoding](https://mlsanity.com/paper/2505.07858/) (2025)
- [HAMburger: Accelerating LLM Inference via Token Smashing](https://mlsanity.com/paper/2505.20438/) (2025)
- [Make Every Draft Count: Hidden State based Speculative Decoding](https://mlsanity.com/paper/2602.21224/) (2026)
- [Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding](https://mlsanity.com/paper/2605.00342/) (2026)
- [PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding](https://mlsanity.com/paper/2605.13319/) (2026)
- [Cacheback: Speculative Decoding With Nothing But Cache](https://mlsanity.com/paper/2511.21699/) (2025)
- [FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding](https://mlsanity.com/paper/2601.00644/) (2026)
- [MMSpec: Benchmarking Speculative Decoding for Vision-Language Models](https://mlsanity.com/paper/2603.14989/) (2026)
- [SSSD: Simply-Scalable Speculative Decoding](https://mlsanity.com/paper/2411.05894/) (2024)
- [Zero-Shot Cross-Domain Code Search without Fine-Tuning](https://mlsanity.com/paper/2504.07740/) (2025)
- [JuDi: Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence](https://mlsanity.com/paper/2601.04766/) (2026)
- [TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts](https://mlsanity.com/paper/2608.22788/) (2026)
- [Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels](https://mlsanity.com/paper/2412.18106/) (2024)
- [Polybasic Speculative Decoding Through a Theoretical Perspective](https://mlsanity.com/paper/2510.26527/) (2025)
- [Fast Inference of Visual Autoregressive Model with Adjacency-Adaptive Dynamical Draft Trees](https://mlsanity.com/paper/2512.21857/) (2025)
- [AdaPLD: Adaptive Retrieval and Reuse for Efficient Model-Free Speculative Decoding](https://mlsanity.com/paper/2606.05742/) (2026)

## Abstract (arXiv)

As Large Language Models (LLMs) have made significant advancements across various tasks, such as question answering, translation, text summarization, and dialogue systems, the need for accuracy in information becomes crucial, especially for serious financial products serving billions of users like Alipay. However, for a real-world product serving millions of users, the inference speed of LLMs becomes a critical factor compared to a mere experimental model. Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our LLM-based scenarios, with lossless generation accuracy. In the traditional inference process, each token is generated sequentially by the LLM, leading to a time consumption proportional to the number of generated tokens. To enhance this process, our framework, named textitlookahead, introduces a textitmulti-branch strategy. Instead of generating a single token at a time, we propose a Trie-based retrieval and verification mechanism to be able to accept several tokens at a forward step. Our strategy offers two distinct advantages: (1) it guarantees absolute correctness of the output, avoiding any approximation algorithms, and (2) the worst-case performance of our approach is equivalent to the conventional process. We conduct extensive experiments to demonstrate the significant improvements achieved by applying our inference acceleration framework. Our framework is widely deployed in Alipay since April 2023, and obtain remarkable 2.66x to 6.26x speedup. Our code is available at https://github.com/alipay/PainlessInferenceAcceleration.