Delivering Modern Networking for the Age of AI with Arrcus

Arrcus outlined its approach to networking for AI, presenting a unified, programmable fabric designed to span training and inferencing workloads from the edge to the data center and public cloud. Sanjay Kumar and Keyur Patel described the company’s hardware-agnostic network operating system, its broad merchant silicon and DPU support, the Arrcus Inference Network Fabric, and the automation and observability capabilities built around it. The session took place at Networking Field Day 41, where Kumar, who leads product management and marketing, was joined by CTO and co-founder Keyur Patel and AI solutions chief architect Nalin Pai.

Kumar framed the problem around AI’s need for deterministic, low-latency networking that never leaves expensive GPUs waiting, along with security built into the fabric, end-to-end telemetry, and automation across the full lifecycle. Arrcus defines programmability as network-as-code, with OpenConfig, YANG, and NETCONF northbound and support for any merchant silicon southbound. The hardware list includes Broadcom’s XGS and DNX families, NVIDIA Spectrum switches, BlueField-3 DPUs with BlueField-4 now being onboarded, Intel’s NetSec Accelerator, and Fujitsu’s ARM-based Monaka CPU, plus certified images for AWS, Azure, GCP, and OCI and container deployment under Kubernetes. Asked how Arrcus keeps pace with new silicon, Kumar credited its data plane abstraction layer, and Patel added that the layer targets routing on deep-buffer platforms, covering IPv6, SRv6, and MPLS rather than switching alone. Pensando support is on the roadmap.

For training, Arrcus offers scale-out and scale-across designs built on a tuned lossless fabric with adaptive routing, multi-tenancy for GPU-as-a-service operators, and standards support that extends past RoCEv2 and PFC into Ultra Ethernet Consortium work, with scale-up support to follow as those standards solidify. For inferencing, the company introduced the Arrcus Inference Network Fabric, which Kumar described as an inferencing router. Delegates also probed the multi-cloud overlay, and Arrcus confirmed that a single policy, including IPsec encryption between clouds, can be defined once and applied across private and public infrastructure. Kumar closed by claiming total cost of ownership reductions of 40% or more across capex and opex, positioning one hardware-agnostic fabric as the way operators move AI into its commercial phase.