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CEO and co-founder Alex Saroyan discusses the evolution of network engineering in the era of AI. Saroyan highlights that AI networking significantly differs from traditional data center networking due to the massive scale of GPU clusters, ranging from 1,000 to over 50,000 GPUs, and the sheer density of network switches involved. Netris introduces the concept of Network Automation, Abstraction, and Multi-tenancy (NAAM), a technology category designed to succeed SDN and intent-based networking. Unlike previous models that relied on software-based overlays, NAM is engineered for physical, high-performance fabrics where massive traffic volumes make traditional SDN impractical. The presentation emphasizes that Netris has gained significant traction as the first NVIDIA-validated network automation vendor, serving a growing neocloud market of AI factory operators.
The complexity of AI networking is illustrated through the cake of networking layers, which includes front-end, back-end (East-West), and scale-up (NVLink) fabrics. Saroyan explains that even a relatively small cluster of 512 GPUs involves over 1,200 network links, making manual configuration or “fire and forget” approaches impossible. A critical requirement for these operators is multi-tenancy, not just for security, but for operational maintenance. Since operators cannot access tenant data, they must have the ability to dynamically move servers between a tenant environment and a service environment to perform repairs or OS reinstalls. To manage this, Netris utilizes a distributed architecture with agents residing on switches and DPUs, allowing for scalable, consistent, and validated configurations across thousands of nodes without the bottlenecks of centralized automation tools like Ansible.
End-to-end orchestration in a Netris environment covers diverse hardware and protocols, including Ethernet, InfiniBand, and specialized RDMA-over-Converged-Ethernet (RoCE) setups. Netris maintains a vendor-neutral stance, supporting NVIDIA, Arista, and Broadcom-based Sonic switches, as well as integrating with NVIDIA’s UFM for InfiniBand and partitioning NVLink for rack-scale isolation. The session concludes by emphasizing that NAM acts as a bridge between compute orchestrators, such as Kubernetes, CloudStack, or NVIDIA Nico, and the underlying physical network. By providing robust API-level collaboration, Netris enables network engineers to design, simulate, and troubleshoot massive AI fabrics, ensuring that the physical infrastructure can keep pace with the dynamic demands of AI compute and multi-tenant resource allocation.
Personnel: Alex Saroyan
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