Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing

TL;DR

This paper presents an autoregressive-aware split computing framework for deploying large language models on resource-constrained devices, combining novel quantization and optimization techniques to reduce memory, latency, and communication costs.

Contribution

It introduces a new split computing framework with adaptive quantization and optimization strategies specifically designed for autoregressive LLM inference on edge devices.

Findings

Achieves 1.49x inference speedup over state-of-the-art methods.

Reduces communication overhead significantly while maintaining accuracy.

Demonstrates effectiveness across diverse LLMs and hardware platforms.

Abstract

Large language models (LLMs) have achieved near-human performance across diverse reasoning tasks, yet their deployment on resource-constrained Internet-of-Things (IoT) devices remains impractical due to massive parameter footprints and memory-intensive autoregressive decoding. While split computing offers a promising solution by partitioning model execution between edge devices and cloud servers, existing approaches fail to address the unique challenges of autoregressive inference, particularly the iterative token generation process and expanding key-value (KV) cache requirements. This work introduces the first autoregressive-aware split computing framework designed explicitly for LLM deployment on edge devices. Our approach makes three key contributions. First, we develop one-point split compression (OPSC), a mixed-precision quantization scheme that prevents out-of-memory failures by strategically partitioning models into front-end and back-end segments with different precision levels. Second, we propose a two-stage intermediate compression pipeline that combines threshold splitting (TS) and token-wise adaptive bit quantization (TAB-Q) to preserve accuracy-critical activations while dramatically reducing communication overhead. Third, we formulate a unified optimization framework that jointly selects optimal split points, quantization settings, and sequence lengths to satisfy strict memory and latency constraints. Extensive evaluations across diverse LLMs and hardware platforms demonstrate superior performance compared to state-of-the-art quantization methods, including SmoothQuant, OmniQuant, and Atom. The framework achieves a 1.49 inference speedup and significant communication overhead reduction while maintaining or improving model accuracy.