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2025 Data Center Networking
A Collaboration of Converge! Network Digest and AvidThink
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As the world invests multiple hundreds of billions to trillions of dollars in data center build out for AI, what's the role of networking? How should networking serve both AI and Cloud workloads? Will the shift from pre-training to post-training to test-time/inference-time scaling change the data center computing and networking requirements? So many questions, but do we know the answers?
To help our readers, we've captured insights from the leading thinkers in the data center networking ecosystem here in our showcase. The content in the videos and our report highlight the state-of-the-art in networking scale-up, scale-out, and scale-outside. What's scale-outside? Download the report to find out!
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Kevin Deierling, Senior Vice President of Networking at NVIDIA, outlines the company's scale-up and scale-out networking technologies, focusing on their GB200 NVL72 architecture, InfiniBand offerings, and Spectrum-X Ethernet platform. He discusses scale-up and scale-out scenarios, highlighting how their optimized Ethernet offering can support 100K+ GPU clusters.
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Views expressed are those of the presenting individuals and companies and may not necessarily represent views of Converge! Network Digest or AvidThink.
Shekar Ayyar, CEO of Arrcus, outlines how networking infrastructure supports AI’s progression from training to distributed inference applications through their multi-platform networking fabric and operating system. He discusses Arrcus’s IPsec offloading for AI data centers and cost management solutions, while highlighting Ethernet’s expanding role in connecting GPU stacks to edge computing.
Dudy Cohen, VP, Product Marketing at DriveNets, outlines how data centers can support AI workloads through advanced networking solutions that eliminate jitter and packet drop while maintaining low latency. His showcases the DriveNets Network Cloud AI solution, which uses scheduled Ethernet fabric to connect thousands of GPUs through a single hop while operating on cost-effective white box hardware.
Mike Bushong, Vice President of Data Center at Nokia, explains that AI infrastructure goes beyond just hyperscalers and spans multiple markets with distinct geographical constraints and requirements for power and space. He outlines how Nokia is addressing these needs through partnerships, multivendor management, and comprehensive networking solutions.
Kevin Deierling, Senior Vice President of Networking at NVIDIA, outlines the company’s scale-up and scale-out networking technologies, focusing on their GB200 NVL72 architecture, InfiniBand offerings, and Spectrum-X Ethernet platform. He discusses scale-up and scale-out scenarios, highlighting how their optimized Ethernet offering can support 100K+ GPU clusters.
Winnie Wu, Senior Director of Product Marketing at Marvell, describes how data center architectures are adapting to AI server needs through changes in switch placement and rack configurations. She outlines their 7-meter 28-gauge cables supporting 800G breakout connections and their 1.6T product with 32-gauge cables optimized for GPU-to-GPU connectivity.
Annie Liao, Product Management Director at Marvell, presents PCIe Gen 6 retimer solutions and PCIe Gen 7 technology critical for scaling up AI infrastructure. The demonstrations include a three-board PCIe Gen 6 setup with retimer capabilities, and a PCIe Gen 7 system achieving 128G transfer speeds with improved bit error rates through TSMC’s 3nm process.
Tony Chan Carusone, Chief Technology Officer at Alphawave Semi, discusses how AI demand and maturing die-to-die interface standards are accelerating chiplet adoption. He outlines three main applications: connecting compute chiplets with high bandwidth and low latency, linking compute cores to IO chiplets for extended connectivity, and interfacing compute chiplets with optical components for long-distance AI cluster communication.
Paroma Sen, Vice President of Corporate Marketing at Astera Labs, showcases four major data center innovations including PCI Gen 6 solutions, Cosmos software suite, and Leo CXL smart memory controllers 5. She shares the company’s expanding technology portfolio while highlighting their leadership in UALink development.
Sailesh Kumar, CEO of Baya Systems, leads the development of Network on Chip (NoC) technology that addresses data movement challenges in AI compute architectures. Baya has successfully delivered its chiplet-ready foundational product to multiple customers and is expanding into automotive and data center markets.
Amit Sanyal, Senior Director of Data Center Product Marketing at Juniper, shares how Ethernet performance matches InfiniBand in AI clusters through benchmarks from Juniper’s AI lab and major hyperscalers. His analysis shows how leading AI providers select Ethernet for its extensive ecosystem and cost advantages, while highlighting their focus on power efficiency through liquid cooling and advanced switch designs.
Hani Daou, Business Development Manager at Multilane, explains how AI and cloud providers are pushing the boundaries for faster, higher-density network connectivity. He showcases Multilane’s new 200 GB per lane bit error rate tester with a 3M 1.6 terabit active electrical cable, helping vendors advance their high-speed interconnect solutions for AI clusters.
Kurtis Bowman, Chairman of UALink Consortium, is leading the development of an Ultra Accelerator Link technology that enables connecting up to 1,024 GPUs for enhanced AI capabilities. The initiative, supported by major tech companies and over 75 consortium members, aims to create an open standard supporting 200-800 Gbps data rates with low latency communication between GPUs.