Alibaba Cloud claims its new Aegaeon pooling system reduces the number of Nvidia GPUs required to serve large language models by 82% during a multi-month beta test inside its Model Studio marketplace. The result, published in a peer-reviewed paper presented at the 2025 ACM Symposium on Operating Systems (SOSP) in Seoul, suggests that cloud providers may be able to extract significantly more inference capacity from existing silicon, especially in constrained markets like China, where the supply of Nvidia's latest H20s remains limited.

Unlike training-time breakthroughs that chase model quality or speed, Aegaeon is an inference-time scheduler designed to maximize GPU utilization across many models with bursty or unpredictable demand. Instead of pinning one accelerator to one model, Aegaeon virtualizes GPU access at the token level, allowing it to schedule tiny slices of work across a shared pool. This means one H20 could serve several different models simultaneously, with system-wide “goodput” — a measure of effective output — rising by as much as nine times compared to older serverless systems.