Kurtis Bowman, chairman of the UALink Consortium, outlines the direction of UALink 2.0 and explains how an open GPU interconnect is evolving for large AI clusters. The discussion covers protocol and physical-layer separation, in-network compute, improved manageability, and the consortium’s chiplet work aimed at future-scale architectures.
In this session, Bowman reveals the UALink Consortium’s newly released Specification 2.0, which introduces significant architectural improvements including in-network compute capabilities and a modular design that separates protocol layers from physical interfaces. He outlines how this separation enables future speed scalability without requiring software changes, and discusses the consortium’s approach to industry-standard management tools and chiplet integration. With over 115 member companies and products expected in 2027, Bowman positions UALink as a key enabler for next-generation AI infrastructure.
**What You Will Learn:**
– How memory bandwidth limitations impact AI cluster performance and why traditional CPU solutions don’t apply to GPU environments
– The competitive advantages of UALink’s open architecture compared to proprietary interconnect solutions
– Key features in Specification 2.0, including in-network compute and protocol layer separation
– How UALink integrates with industry-standard management tools and UCIE chiplet specifications
– The consortium’s product roadmap and timeline for commercial availability
📚 CHAPTERS:
0:00:00 – Introduction: AI Clusters and the Memory Bandwidth Challenge
0:01:15 – UALink’s Open Architecture and Competitive Advantages
0:02:56 – Consortium Growth and Industry Adoption
0:04:10 – Specification 2.0: Modular Design and In-Network Compute
0:06:00 – Protocol Layer Separation for Future Speed Scalability
0:07:20 – Management Standards and Industry Tool Integration
0:08:46 – Chiplet Specification and UCIE Integration
0:09:56 – Product Roadmap and Future Development Timeline
0:10:42 – Summary: UALink’s Role in AI Infrastructure Evolution
