What is scale-up in AI computing?A network architecture within an AI compute cluster that maximizes intra-cluster communication bandwidth and minimizes latency.
Scale-up, also known as compute fabric, compute cluster fabric, memory-semantic fabric, or back-end network, refers to the network architecture within AI compute clusters. It operates within a compute cluster, aiming to maximize intra-cluster communication bandwidth and minimize latency among tightly packed computing resources, such as GPUs. Scale-up networks are designed for ultra-high interconnect bandwidth coupled with low latency, often extending beyond a single rack and requiring advanced interconnect solutions like in-package optical I/O.
As AI models grow and networks must extend beyond the confines of a single rack, in-package optical I/O represents a transformative solution to AI data bottlenecks. In-package optical I/O can increase the size of clusters allowing more computing resources with high-bandwidth data transfers.
Related:
AI Scale-Up Architecture with Optical I/O | Solution Overview
Improving the Scale-Up Performance of AI Clusters with Optical I/O | Video
Co-Packaged Optics Step Into the Spotlight | Blog
AI and Optical I/O: Overcoming AI Scaling Challenges by Dispelling 3 Current Optical I/O Misconceptions | Blog
AI Scale-up Networking Trends | Article
How Optical I/O is Enabling the Future of Generative AI: A Q&A with Vladimir Stojanovic | Blog
Artificial Intelligence | Solution Overview
Scale-Out | Glossary
In-Package Optical I/O | Glossary