How a Smart Pooling System Turns 213 GPUs into the Power of 1,192, Unlocking Massive Inference Gains
- Dramatic Resource Savings: Alibaba Cloud’s innovative Aegaeon system reduced Nvidia H20 GPU requirements by 82% in real-world tests, dropping from 1,192 to just 213 units while serving dozens of large language models (LLMs) up to 72 billion parameters.
- Token-Level Innovation: By virtualizing GPU access at the token level, Aegaeon enables one accelerator to handle multiple models simultaneously, boosting effective output (goodput) by up to 9 times compared to traditional serverless setups.
- Broader Implications for AI Infrastructure: This inference-time breakthrough, detailed in a peer-reviewed SOSP 2025 paper, could help cloud providers worldwide maximize existing hardware, especially in supply-constrained markets like China amid U.S. export restrictions.
In the high-stakes world of artificial intelligence, where computational power is both the engine and the bottleneck, Alibaba Cloud has unveiled a game-changer. Their new Aegaeon pooling system promises to redefine how we deploy large language models, squeezing unprecedented efficiency from Nvidia‘s GPUs. Presented at the prestigious 2025 ACM Symposium on Operating Systems (SOSP) in Seoul, the system’s peer-reviewed paper reveals a multi-month beta test inside Alibaba’s Model Studio marketplace that slashed GPU usage by 82%. This isn’t just a tweak—it’s a fundamental shift in inference scheduling that could ease the global scramble for AI hardware.
At its core, Aegaeon addresses a persistent pain point in AI deployment: underutilized GPUs. Traditional setups often dedicate a single accelerator to one model, leading to idle time when demand is bursty or unpredictable. Unlike training-focused innovations that prioritize model quality or raw speed, Aegaeon zeroes in on inference—the phase where models generate responses in real time. By virtualizing GPU access down to the granular level of individual tokens (the basic units of text in LLMs), the system dynamically slices and schedules workloads across a shared pool. Imagine a single Nvidia H20 GPU juggling multiple models at once, serving requests for everything from chatbots to code generators without breaking a sweat. The result? System-wide “goodput”—a metric blending output quality and speed—skyrocketed by as much as nine times over older serverless architectures.
The real-world proof came during an extensive production trial, co-authored by researchers from Peking University and Alibaba’s infrastructure team, including CTO Jingren Zhou. Over several months, the system supported a diverse lineup of LLMs, some scaling up to 72 billion parameters. What started as a need for 1,192 H20 GPUs dwindled to a mere 213, freeing up vast resources for other tasks. Reports from the South China Morning Post highlight that these tests relied on Nvidia’s H20 chips, which remain one of the few high-end accelerators legally accessible to Chinese firms under tightening U.S. export controls. This constraint has forced innovators like Alibaba to get creative, turning potential limitations into efficiency triumphs.
Diving deeper, Aegaeon’s magic stems from two powerhouse techniques. First, it packs multiple models onto a single GPU, maximizing occupancy without compromising performance. Second, a token-level autoscaler allocates compute on the fly as outputs are generated, ditching the inefficiency of reserving entire resources per request. In head-to-head benchmarks, Aegaeon outperformed rivals like ServerlessLLM and MuxServe by margins of 1.5 to 9 times in goodput. These aren’t lab curiosities; they’re metrics from a live environment handling unpredictable AI workloads, proving the system’s robustness.
Of course, no breakthrough is without caveats. The paper doesn’t detail the underlying network fabric, though Alibaba’s custom eRDMA elastic RDMA setup and history of tightly integrated GPU stacks suggest the gains are tailored to their ecosystem. Replicating this elsewhere might require similar vertical optimization, potentially challenging for providers without Alibaba’s in-house control. Yet, the broader perspective is exhilarating: as inference demand surges globally—fueled by everything from enterprise chat interfaces to creative tools—hyperscalers like AWS, Google Cloud, and Microsoft Azure could adapt similar strategies to stretch their accelerator fleets.
In a landscape where Nvidia GPUs are the gold standard but increasingly scarce, Aegaeon’s 82% reduction isn’t just a win for Alibaba; it’s a blueprint for sustainable AI scaling. By focusing on smarter utilization rather than endless hardware buys, it paves the way for more accessible, cost-effective intelligence. As the SOSP 2025 presentation underscores, the future of AI may lie not in building bigger, but in using better—unlocking hidden potential in the silicon we already have.