HexGen: Generative Inference of Large Language Model over Heterogeneous Environment

Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and crossdatacenter setting to mitigate the substantial inference costs typically associated with a single centralized datacenter. Towards this end, we propose HEXGEN, a flexible distributed inference engine that uniquely supports the asymmetric partition of generative inference computations over both tensor model parallelism and pipeline parallelism and allows for effective deployment across diverse GPUs interconnected by a fully heterogeneous network. We further propose a sophisticated scheduling algorithm grounded in constrained optimization that can adaptively assign asymmetric inference computation across the GPUs to fulfill inference requests while maintaining acceptable latency levels. We conduct an extensive evaluation to verify the efficiency of HEXGEN by serving the state-of-the-art LLAMA-2 (70B) model. The results suggest that HEX-GEN can choose to achieve up to 2.3× lower latency deadlines or tolerate up to 4× more request rates compared with the homogeneous baseline given the same budget. Our implementation is available at https://github.com/ Relaxed-System-Lab/HexGen .