Serving generative inference of the large-scale foundation model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and decentralized setting to mitigate the substantial inference costs typically associated with centralized data centers. 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 HexGen can choose to achieve up to 2.3 times lower latency deadlines or tolerate up to 4 times more request rates compared with the homogeneous baseline given the same budget.

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