Uploaded December 2025 | Updated September 2026, 2 weeks ago
Jingren Zhou '01SEAS, 04'SEAS is the Chief Technology Officer at Alibaba Cloud, where he drives technology innovation and product development across a wide range of cloud computing services. He also leads the development of AI foundation models, such as Qwen and Wan models, and their applications in diverse business applications within Alibaba Cloud. Prior to this role, he played a key role in building Alibaba’s cloud-scale distributed data analytics platform and developing advanced techniques for personalized search, product recommendation, and advertising on Alibaba’s e-commerce platform. Before joining Alibaba, he was a veteran at Microsoft, focusing on big data and database research and development. His research interests include cloud computing, distributed systems, databases, and large-scale machine learning. He has served as PC co-chair and core committee member for many academic conferences and technical forums. He received his PhD in Computer Science from Columbia University.
He is a Fellow of ACM and IEEE.
"Building Foundation Models at Scale: System Experiences and Challenges"
The rapid evolution of AI has led to the emergence of massive and complex foundation models that require enormous computational resources, making efficient training and inference systems essential. Training such models requires large-scale distributed computation, effective overlap of computation and communication, sophisticated parallelization strategies, and robust fault-tolerant mechanisms. Inference systems, on the other hand, must support diverse workloads with varying service-level agreements
(SLAs), rapidly integrate engineering optimizations, and carefully balance trade-offs among throughput, latency, cost, and availability, particularly in distributed environments. In this talk, I will discuss the major systems challenges in building large-scale foundation models, focusing on our experiences developing Qwen (large language models) and Wan (video generative models). I will also present ongoing research
and system designs that enhance the efficiency of training and inference at scale, enabling more effective management of complex AI workloads in cloud environments.
Jingren Zhou '01SEAS, 04'SEAS is the Chief Technology Officer at Alibaba Cloud, where he drives technology innovation and product development across a wide range of cloud computing services. He also leads the development of AI foundation models, such as Qwen and Wan models, and their applications in diverse business applications within Alibaba Cloud. Prior to this role, he played a key role in building Alibaba’s cloud-scale distributed data analytics platform and developing advanced techniques for personalized search, product recommendation, and advertising on Alibaba’s e-commerce platform. Before joining Alibaba, he was a veteran at Microsoft, focusing on big data and database research and development. His research interests include cloud computing, distributed systems, databases, and large-scale machine learning. He has served as PC co-chair and core committee member for many academic conferences and technical forums. He received his PhD in Computer Science from Columbia University.
He is a Fellow of ACM and IEEE.
"Building Foundation Models at Scale: System Experiences and Challenges"
The rapid evolution of AI has led to the emergence of massive and complex foundation models that require enormous computational resources, making efficient training and inference systems essential. Training such models requires large-scale distributed computation, effective overlap of computation and communication, sophisticated parallelization strategies, and robust fault-tolerant mechanisms. Inference systems, on the other hand, must support diverse workloads with varying service-level agreements
(SLAs), rapidly integrate engineering optimizations, and carefully balance trade-offs among throughput, latency, cost, and availability, particularly in distributed environments. In this talk, I will discuss the major systems challenges in building large-scale foundation models, focusing on our experiences developing Qwen (large language models) and Wan (video generative models). I will also present ongoing research
and system designs that enhance the efficiency of training and inference at scale, enabling more effective management of complex AI workloads in cloud environments.
