SpeContext: Enabling Efficient Long-context Reasoning with Speculative Context Sparsity in LLMs

TL;DR

SpeContext tackles the bottleneck of long-context reasoning in LLMs by introducing a DLM-based retrieval paradigm. It presents a three-pronged design: a lightweight retrieval head that uses head-level attention from a distilled LM, an asynchronous prefetch dataflow with elastic loading to minimize KV transfer, and an adaptive memory management model to maximize GPU memory usage. The approach achieves substantial throughput improvements (up to 24.89x in cloud and 10.06x in edge) with negligible accuracy loss across long-context input and reasoning tasks, pushing the Pareto frontier of speed and accuracy. The work demonstrates the practicality of information-theoretic alignment between distilled and original LLMs to optimize retrieval, and it offers a scalable blueprint for resource-constrained deployments of long-context reasoning systems.

Abstract

In this paper, we point out that the objective of the retrieval algorithms is to align with the LLM, which is similar to the objective of knowledge distillation in LLMs. We analyze the similarity in information focus between the distilled language model(DLM) and the original LLM from the perspective of information theory, and thus propose a novel paradigm that leverages a DLM as the retrieval algorithm. Based on the insight, we present SpeContext, an algorithm and system co-design for long-context reasoning. (1) At the algorithm level, SpeContext proposes lightweight retrieval head based on the head-level attention weights of DLM, achieving > 90% parameters reduction by pruning the redundancy. (2) At the system level, SpeContext designs an asynchronous prefetch dataflow via the elastic loading strategy, effectively overlapping KV cache retrieval with the LLM computation. (3) At the compilation level, SpeContext constructs the theoretical memory model and implements an adaptive memory management system to achieve acceleration by maximizing GPU memory utilization. We deploy and evaluate SpeContext in two resourceconstrained environments, cloud and edge. Extensive experiments show that, compared with the Huggingface framework, SpeContext achieves up to 24.89x throughput improvement in cloud and 10.06x speedup in edge with negligible accuracy loss, pushing the Pareto frontier of accuracy and throughput.

Figures (11)

• Figure 1: (a)(b) Pareto frontiers on KV cache selection in long-context input and reasoning scenarios.
• Figure 2: Overview of SpeContext. (a) Three challenges in existing algorithm paradigm in the long-context reasoning scenario. (b) Key Insight: Distilled language model exhibits similar information focus. (c) Contributions from Section \ref{['sec:T1']} to Section \ref{['sec:T3']}
• Figure 3: Architecture of SpeContext.
• Figure 4: (a) Inference dataflow of LLM. (b) Existing works on KV cache optimization.
• Figure 5: (a) We design the lightweight retrieval head by pruning redundancy and adopt the head-level attention weights for selection. (b)$\sim$(e) The detailed implementations of four attention mechanisms supported by the lightweight retrieval head.