SpecOffload: Unlocking Latent GPU Capacity for LLM Inference on Resource-Constrained Devices

2025-05-15

AI Summary

To address GPU memory exhaustion during large language model (LLM) inference on resource-constrained devices—causing frequent CPU-GPU data transfers and underutilized compute units—this paper proposes a synergistic optimization framework integrating speculative decoding with weight offloading. The core innovation deploys a lightweight draft model on otherwise idle GPU compute resources and embeds it within a hierarchical tensor offloading pipeline. Through dynamic execution orchestration and interleaved scheduling between the draft and target models, acceleration is achieved with zero additional overhead. Crucially, the method circumvents I/O bottlenecks inherent in conventional offloading schemes. Without requiring extra hardware, it improves GPU core utilization by 4.49× and inference throughput by 2.54×, effectively unlocking latent GPU compute capacity on memory-limited devices.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Efficient LLM inference on resource-constrained devices presents significant challenges in compute and memory utilization. Due to limited GPU memory, existing systems offload model weights to CPU memory, incurring substantial I/O overhead between the CPU and GPU. This leads to two major inefficiencies: (1) GPU cores are underutilized, often remaining idle while waiting for data to be loaded; and (2) GPU memory has low impact on performance, as reducing its capacity has minimal effect on overall throughput.In this paper, we propose SpecOffload, a high-throughput inference engine that embeds speculative decoding into offloading. Our key idea is to unlock latent GPU resources for storing and executing a draft model used for speculative decoding, thus accelerating inference at near-zero additional cost. To support this, we carefully orchestrate the interleaved execution of target and draft models in speculative decoding within the offloading pipeline, and propose a planner to manage tensor placement and select optimal parameters. Compared to the best baseline, SpecOffload improves GPU core utilization by 4.49x and boosts inference throughput by 2.54x. Our code is available at https://github.com/MobiSense/SpecOffload .

Problem

• Enables efficient LLM inference on resource-constrained devices
• Reduces GPU idle time and I/O overhead during offloading
• Optimizes GPU memory and core utilization for speculative decoding

Innovation

• Speculative decoding embedded in offloading pipeline
• Interleaved execution of target and draft models
• Planner for tensor placement and parameter optimization