FlexEE: Self-Speculative and KV-Compatible Early Exiting for Offloading-Aware LLM Inference

Abstract:Large language model (LLM) inference is often constrained by both computation and memory, especially in offloading-based deployments where model weights are transferred across memory hierarchies during autoregressive decoding. In this setting, reducing the number of executed layers can lower per-token latency while also avoiding costly weight movement. Motivated by this observation, we present FlexEE, an early exiting framework for resource-constrained and offloading-based LLM inference. FlexEE makes early exiting practical for LLM decoding through layer-wise exit supervision for reliable intermediate-layer prediction, self-speculative decoding over a Top-K local vocabulary for low-cost exit decisions, and dynamic hidden state management for KV-cache-correct and memory-aware execution. Across generative and downstream tasks, FlexEE enables efficient early exit with minimal accuracy degradation, delivering up to 1.27$\times$/3.16$\times$ and 1.25$\times$/2.83$\times$ end-to-end speedups on Llama2-7B and Llama3-8B under 0\%/50\% weight offloading, respectively.

Artificial Intelligence (cs.AI)

arXiv:2609.17008 [cs.AI]

(or arXiv:2609.17008v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.17008 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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