AMD AI NIC™ Technology Driving AI-Ready Ethernet

Building Networks for Evolving AI Workloads

AI workloads continue to evolve across training, inference, distributed inference and agentic AI with each workload placing a unique requirement on the infrastructure. Having a flexible infrastructure that can adapt to future workloads is critical to keeping pace with AI. As organizations build larger and more distributed AI systems, networking has become a critical determinant of overall system performance.

The need for both performance and flexibility has helped make Ethernet the networking foundation of choice for modern AI data centers. Its broad ecosystem, open standards, and ability to continuously evolve have enabled widespread adoption, with Gartner forecasting that more than 80% of hyperscalers will choose Ethernet for AI workloads by 2028. However, large-scale AI training places fundamentally different demands on the network than traditional cloud and storage workloads. Keeping hundreds of thousands of GPUs tightly synchronized requires fast recovery from transient congestion and path failures, leveraging load balancing like packet spray and source routing, as well as other capabilities that traditional Ethernet transports such as RoCEv2 were not originally designed to provide.

That's why organizations across the AI ecosystem, including OpenAI, Microsoft, Meta, and AMD, are developing new Ethernet transports optimized for AI workloads. The fully programmable AI NIC architecture from AMD has helped accelerate the development, testing and validation of these transports, enabling innovation without waiting for new hardware generations. In just 16 months, three new major AI-ready Ethernet transports emerged, highlighting both the pace of AI networking innovation and the need for infrastructure that can evolve alongside it.

Customer Choice at the Pace of AI

The emergence of multiple AI-ready transports reflects an important reality: no single transport is likely to be the right answer for every AI deployment. Different workloads, architectures, and operational requirements can favor different approaches to congestion control, load balancing, and failure recovery.

To evolve at the pace of AI, organizations need the flexibility to adopt the transport that best fits their environment rather than being limited to a single hard-coded networking approach. AMD AI NIC technology is the only fully programmable AI NIC architecture available today, giving customers the ability to implement new transport capabilities and optimizations on existing hardware without waiting for the next hardware generation.

This approach also leverages existing investment to help accelerate future generations of interconnects to production. Customers can develop, validate, and deploy transports on AMD Pensando™ Pollara 400 AI NICs and carry that work forward to AMD Pensando™ Vulcano 800 AI NICs, preserving software investments and operational expertise across generations. Combined with the open ecosystem approach from AMD, organizations gain greater choice in how they build and evolve AI networks.

Driving the Next Generation of Ethernet Transports

The industry's response to these challenges can already be seen across a growing set of AI-ready Ethernet transports. Developed by hyperscalers, AI leaders, and industry consortiums, these transports are helping improve congestion control, resiliency, efficiency, and scalability for large-scale AI deployments. Together, they demonstrate how an open ecosystem is accelerating innovation across AI networking.

The Ultra Ethernet Consortium brings together more than 100 industry members to develop an open, interoperable Ethernet architecture purpose-built for AI and HPC environments. AMD was a founding member of the consortium and authored Network-Signaled Congestion Control (NSCC), now included within the UEC congestion management specification. AMD also delivered one of the first AI NICs capable of supporting the UEC transport specification, providing a platform for ecosystem development, testing, and validation.

Multipath Reliable Connection (MRC) was initially developed to improve network utilization and resiliency for large-scale AI environments. While OpenAI played a leading role in its development, the specification also benefited contributions across the broader ecosystem, including AMD, Microsoft, and other industry participants before being contributed to OCP. MRC is already deployed at Microsoft and Oracle to train OpenAI's frontier models.

AMD co-led authorship of the specification and participated throughout its development. In addition to contributing congestion control technology adopted across multiple transport efforts, AMD helped accelerate development through its programmable AI NIC architecture, providing a platform for implementation, testing, and validation as new transport innovations emerged.

Meta developed MetaRoCE to support large-scale AI training environments and the networking requirements behind its AI workloads. To accelerate deployment and validation, Meta worked directly with AMD to implement the transport on AMD AI NIC technology. Meta validated MetaRoCE on 400G AMD AI NIC technology, demonstrating how programmable networking platforms can help bring new transport innovations from development into production more quickly.

Evolving at the Pace of AI

AI workloads, system architectures, and networking requirements will continue to evolve. The rapid emergence of new Ethernet transports demonstrates both the pace of innovation across the AI ecosystem and the importance of maintaining flexibility as infrastructure requirements mature and evolve.

AMD AI NIC technology is designed to help organizations evolve at the pace of AI. Through a fully programmable architecture, support for open standards, and collaboration across the industry, AMD helps customers adopt, validate, and scale the networking technologies best suited to their environments while preserving investments across generations.

As AI continues to push the boundaries of infrastructure, openness, programmability, and ecosystem collaboration will remain essential to building the next generation of AI networks.

Learn more about AMD AI NIC technology today.