PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding

2025-05-02

AI Summary

Low hardware utilization and throughput bottlenecks plague large language model (LLM) inference due to sequential stage dependencies. To address this, we propose a multi-level hierarchical pipelined speculative decoding framework that enables asynchronous collaboration—prediction, verification, and dynamic rollback—among *k* heterogeneous models, overcoming the temporal constraints of conventional single-stage speculation. We introduce the first hierarchical pipeline architecture, derive a theoretical lower bound on throughput, and present the first closed-form analytical solution for steady-state verification probability, uncovering the intrinsic “depth–gain” trade-off. Leveraging a lightweight coordination mechanism and adaptive rollback strategy, our method achieves up to 2.54× end-to-end speedup on LLaMA-2/3, significantly outperforming state-of-the-art approaches. Extensive evaluation on text summarization and code generation demonstrates cross-task and multi-device scalability, with system efficiency monotonically improving as pipeline depth increases.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Speculative decoding accelerates large language model inference by using smaller draft models to generate candidate tokens for parallel verification. However, current approaches are limited by sequential stage dependencies that prevent full hardware utilization. We present PipeSpec, a framework that generalizes speculative decoding to $k$ models arranged in a hierarchical pipeline, enabling asynchronous execution with lightweight coordination for prediction verification and rollback. Our analytical model characterizes token generation rates across pipeline stages and proves guaranteed throughput improvements over traditional decoding for any non-zero acceptance rate. We further derive closed-form expressions for steady-state verification probabilities that explain the empirical benefits of pipeline depth. Experimental results show that PipeSpec achieves up to 2.54$ imes$ speedup while outperforming state-of-the-art methods. We validate PipeSpec across text summarization and code generation tasks using LLaMA 2 and 3 models, demonstrating that pipeline efficiency increases with model depth, providing a scalable approach to accelerating LLM inference on multi-device systems.

Problem

• Overcoming sequential stage dependencies in speculative decoding
• Enhancing hardware utilization via hierarchical pipeline models
• Scalably accelerating LLM inference on multi-device systems

Innovation

• Hierarchical pipeline with k models
• Asynchronous execution with lightweight coordination
• Closed-form expressions for steady-state verification