Alibaba Cloud Unveils Aegaeon: 82% GPU Reduction

Alibaba Cloud’s Aegaeon: Dramatically Slashing Nvidia GPU Needs with Innovative Pooling

Alibaba Cloud has unveiled a breakthrough GPU pooling system, Aegaeon, which it claims reduces the number of Nvidia GPUs required to serve artificial intelligence (AI) workloads by 82%. The announcement, based on research presented at the 31st Symposium on Operating Systems Principles (SOSP) in Seoul, comes as cloud providers globally face soaring demand for AI compute—and escalating costs associated with GPU procurement and energy consumption. The Aegaeon system was beta-tested in Alibaba Cloud’s AI model marketplace for over three months, demonstrating a reduction from 1,192 Nvidia H20 GPUs to just 213 GPUs needed to serve dozens of large language models (LLMs) with up to 72 billion parameters.

Background

Cloud providers like Alibaba Cloud and ByteDance’s Volcano Engine serve thousands of AI models simultaneously, responding to a flood of API calls from users worldwide. However, demand is highly uneven: a small fraction of models—such as Alibaba’s own Qwen and DeepSeek—are in constant use for inference, while the vast majority are only sporadically accessed. This creates significant inefficiency, with 17.7% of GPUs allocated to serve just 1.35% of requests in Alibaba Cloud’s marketplace. Traditional approaches assign fixed GPU resources to each model, leading to underutilization and higher operational costs.

Key Features of Aegaeon

Aegaeon addresses this inefficiency through multi-model serving and granular auto-scaling at the token level, enabling dynamic sharing of GPU resources across many models. The system’s core innovations include:

Token-level Scheduling: Instead of dedicating whole GPUs or even large chunks of GPU memory to individual models, Aegaeon schedules compute at the granularity of individual tokens—the basic units of text processed by LLMs. This allows multiple models to share the same GPU, dramatically increasing utilization.
Dynamic Resource Allocation: Aegaeon continuously monitors demand, scaling GPU resources up or down in real time to match the actual workload. This avoids the waste of static allocations and ensures that popular models get the resources they need when they need them.
Efficient Model Switching: The system minimizes the overhead of switching between models, a critical factor given the latency-sensitive nature of AI inference services.
Proven Results: In Alibaba Cloud’s marketplace, Aegaeon reduced the number of GPUs required from 1,192 to 213—an 82% cut—while maintaining service quality for dozens of LLMs, some with up to 72 billion parameters. This translates directly into lower capital and operational expenses for Alibaba and, potentially, its customers.

Technical and Industry Context

GPU pooling is not a new concept, but previous attempts have struggled with the challenges of fine-grained scheduling, model-switching overhead, and maintaining low latency. Aegaeon’s token-level approach is a significant advance, as highlighted by the research team from Peking University and Alibaba Cloud, including Alibaba CTO Zhou Jingren. The system is described as “the first work to reveal the excessive costs associated with serving concurrent LLM workloads on the market.”

Meanwhile, Alibaba Cloud is also investing in Compute Express Link (CXL) memory pooling for databases like PolarDB, enabling disaggregated, large-scale memory resources that can be dynamically allocated across servers. While CXL focuses on memory rather than GPUs, it reflects a broader trend toward resource disaggregation and pooling in cloud infrastructure—a direction that could further reduce costs and increase flexibility for AI and other demanding workloads.

Industry Impact

The implications of Aegaeon’s success are profound for the cloud and AI industry:

Cost Savings: An 82% reduction in GPU requirements could save cloud providers hundreds of millions of dollars annually in hardware, energy, and data center costs. These savings may eventually be passed on to customers, lowering the barrier to entry for AI innovation.
Sustainability: By drastically cutting the number of GPUs needed, Aegaeon also reduces the carbon footprint associated with manufacturing and operating these energy-intensive chips.
Competitive Advantage: Alibaba Cloud positions itself at the forefront of efficient AI infrastructure, a critical differentiator as cloud providers vie for dominance in the global AI market.
Broader Adoption: If the technology proves robust and scalable, other major cloud providers may adopt similar pooling systems, accelerating industry-wide efficiency gains.

Challenges and Considerations

While Aegaeon’s results are impressive, several questions remain:

Latency and Reliability: Maintaining low latency and high reliability while dynamically sharing GPU resources across many models is technically challenging. The system’s performance under peak, unpredictable loads will be closely watched.
Model Compatibility: Not all AI models may be equally amenable to token-level scheduling. Further research is needed to understand the breadth of models and workloads that can benefit from this approach.
Vendor Lock-in: As with many cloud innovations, there is a risk of vendor lock-in if Aegaeon becomes a proprietary standard for Alibaba Cloud. Open-sourcing or standardizing the technology could mitigate this concern.
Regulatory and Security Implications: Dynamic resource sharing raises questions about data isolation and security, especially in multi-tenant environments.

Future Directions

Alibaba Cloud’s Aegaeon represents a major step toward efficient, scalable, and sustainable AI infrastructure. The company is likely to continue refining the system, expanding its deployment, and exploring integrations with other resource-pooling technologies like CXL memory. As AI workloads grow in complexity and scale, innovations like Aegaeon will be essential to keeping cloud services affordable, reliable, and environmentally responsible.

For the broader industry, Aegaeon sets a new benchmark for GPU utilization in AI serving. Its success could spur a wave of innovation in resource pooling, disaggregation, and dynamic scheduling across the cloud computing landscape.