ALTO: An Efficient Network Orchestrator for Compound AI Systems

Keshav Santhanam, Deepti Raghavan, Muhammad Shahir Rahman, Thejas Venkatesh, Neha Kunjal, Pratiksha Thaker, Philip Levis, Matei Zaharia
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computation and Language (cs.CL), Distributed, Parallel, and Cluster Computing (cs.DC), Information Retrieval (cs.IR)
2024-03-07 00:00:00
We present ALTO, a network orchestrator for efficiently serving compound AI systems such as pipelines of language models. ALTO achieves high throughput and low latency by taking advantage of an optimization opportunity specific to generative language models: streaming intermediate outputs. As language models produce outputs token by token, ALTO exposes opportunities to stream intermediate outputs between stages when possible. We highlight two new challenges of correctness and load balancing which emerge when streaming intermediate data across distributed pipeline stage instances. We also motivate the need for an aggregation-aware routing interface and distributed prompt-aware scheduling to address these challenges. We demonstrate the impact of ALTO's partial output streaming on a complex chatbot verification pipeline, increasing throughput by up to 3x for a fixed latency target of 4 seconds / request while also reducing tail latency by 1.8x compared to a baseline serving approach.
PDF: ALTO: An Efficient Network Orchestrator for Compound AI Systems.pdf
Empowered by ChatGPT