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

May 13, 2024ยท
Xuanlei Zhao
,
Bin Jia
,
Haotian Zhou
Ziming Liu
Ziming Liu
,
Shenggan Cheng
,
Yang You
ยท 0 min read
Abstract
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.
Type
Publication
In MLSys 2024, Proceedings of Machine Learning and Systems