HeteGen: Efficient Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

Part of Proceedings of Machine Learning and Systems 6 (MLSys 2024) Conference

Bibtex Paper


ZHAO XUANLEI, Bin Jia, Haotian Zhou, Ziming Liu, Shenggan Cheng, Yang You


In recent times, the emergence of Large Language Models (LLMs) has resulted in increasingly larger model size, posing challenges for inference on low-resource devices. Prior approaches have explored offloading to facilitate low-memory inference but often suffer from efficiency due to I/O bottlenecks. To achieve low-latency LLMs inference on resource-constrained devices, we introduce HeteGen, a novel approach that presents a principled framework for heterogeneous parallel computing using CPUs and GPUs. Based on this framework, HeteGen further employs heterogeneous parallel computing and asynchronous overlap for LLMs to mitigate I/O bottlenecks. Our experiments demonstrate a substantial improvement in inference speed, surpassing state-of-the-art methods by over 317\% at most.