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2026 Data Center Networking

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2026 Data Center Networking

Welcome to the NextGenInfra Data Center Networking for AI and Cloud Showcase!

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? Will the shift in focus from pre-training to post-training to test-time/inference-time scaling change the data center computing and networking requirements? What's the latest in scale-up, scale-out, and scale-across networking.

To help our readers sort through the rapid changes in the space, 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 across the three domains of networking. Check it out!

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Data Center Networking for AI Showcase

How is AI Impacting Data Center Networking?

Check out perspectives from executives in the data center networking space, including Hasan Siraj of Broadcom, Dudy Cohen of DriveNets, Mansour Karam of Aria Networks, Aravind Srikumar of Upscale AI, Sanjay Kumar & Keyur Patel of Arrcus, Manish Gulyani of Nokia, Rishi Chugh of Marvell, and Yuval Bachar of ECL. They discuss the infrastructure challenges brought by AI pre-training, fine-tuning, and inferencing in data centers. Watch our highlights reel and then dig into the details by picking your favorite videos in the showcase below.

Reader feedback on our latest report: “Appreciate the broad coverage and unique insights.” Download our 2026 Data Center Networking report now and send us your feedback!

Content from Sponsors and Thought Leaders

Views expressed are those of the presenting individuals and companies and may not necessarily represent views of Converge! Network Digest or AvidThink.

 Arrcus

How Distributed AI Workloads Are Reshaping Network Architecture

Sanjay Kumar, VP of Products and Marketing, and Keyur Patel, CTO at Arrcus, discuss how the distributed nature of AI workloads is fundamentally changing network architecture, with Ethernet becoming a first-class citizen in AI networks for both training and inferencing across increasingly heterogeneous hardware environments.

 DriveNets

Ethernet Fabrics for AI Data Centers

Dudy Cohen, VP of Product Marketing at DriveNets, explains that while networking represents only 10% of AI infrastructure costs, it causes 80% of deployment challenges, advocating for Ethernet-based solutions with scheduling capabilities that can exceed InfiniBand performance. He emphasizes the importance of working with innovative vendors who have deep expertience.

 Marvell

Big Outlook for XPU-Attach

Will Chu, SVP and GM, Custom Cloud Solutions Business Unit at Marvell, discusses the company’s expansion into XPU attach solutions, where Marvell customizes all components within the XPU tray beyond the XPU. The custom solutions include CXL-enabled memory for expansion and near-memory compute, security devices for AI infrastructure management, and high-performance NICs built on Marvell’s SerDes and other IP platforms.

 Nokia

Linear Pluggable Optics for Data Center Efficiency

Neel Patel, GM of Optical Networking Component Solutions at Nokia, discusses the power consumption challenges of advancing optical modules in data centers, where 1.6T modules consume nearly double the power of 800G modules, potentially adding 1KW to a 64-port switch. He explains how Linear Pluggable Optics (LPO) addresses this by leveraging switch ASIC DSPs reducing power consumption.

 Upscale AI

AI Data Centers Need Purpose-Built Networks

Aravind Srikumar, SVP, Product at Upscale AI, explains how AI workloads require completely lossless, synchronized networks. He shares how Upscale AI delivers AI-specific networking silicon, systems, and software purpose-built for both scale-up and scale-out environments, rather than adapting general-purpose infrastructure.

 Aria Networks

Rethinking AI Networks from First Principles

Mansour Karam, Founder and CEO of Aria Networks, presents their Deep Networking approach centered on performance optimization of AI clusters through fine-grain telemetry. The company’s full-stack architecture combines hardware innovations like microsecond-resolution telemetry embedded in ASICs with specialized AI and agentic capabilities with in-built domain expertise.

 Ayar Labs

Optical Interconnects for AI

Vishal Chandrasekar, Director of Product Management at Ayar Labs, presents an optical interconnect solution that scales GPU and switch connectivity through a chip-to-system approach, featuring optical engines fabricated on TSMC’s platform and multi-chip packages that integrate eight optical engines to replace copper implementations. The solution, developed with partners including FOCI, Browave, Wiwynn, Alchip, and GUC, targets 2028 deployment.

 Broadcom

Ethernet for AI Networking: Scaling to 100,000+ XPUs

Hasan Siraj, Head of Software Products/Ecosystem at Broadcom, presents Broadcom’s product portfolio including the Tomahawk Ultra 51.2 terabit switch, Stormhawk 6 100 terabit switch, Thor Ultra 800 gigabit NIC, and Jericho 4 with deep buffers and line-rate encryption for scale-up, scale-out, and scale-across xPU data center networking

 Clockwork.io

Solving GPU Cluster Inefficiency: From 30% to Peak AI Performance

Suresh Vasudevan, CEO of Clockwork.io, explains how GPU clusters for AI training operate at only 30-50% efficiency due to poor inter-GPU communication, availability issues, and tail latency from slow straggler GPUs. He explains how nanosecond-precision clock synchronization can be leveraged to address availability and latency issues across Infiniband or RoCE networks.

 Ciena

AI Networking at Scale: GPU Cluster Interconnect Solutions for Data Centers

Helen Xenos, Senior Director of Portfolio Marketing at Ciena, presents the company’s innovations for GPU cluster interconnects including the Nitro linear retimer driver for active copper cables and the Vesta 200 6.4T CPX optical engine.

 Credo

MicroLEDs for AI Data Center Connectivity at Scale

LK Bhupathi, AVP, Product at Credo, discusses the company’s expansion into microLED-based technologies. He explains that microLED solutions use dense arrays of LEDs operating at modest speeds to deliver superior reliability, resilience through spare channel failover, and low single-digit pJ per bit power consumption.

 Ethernet Alliance, Intel

Industry's First 224G Interoperability Testing

David Rodgers, Technical Business Development Manager with the Ethernet Alliance, and Sam Johnson, Link Applications Engineering Manager at Intel and High-Speed Networking Chair, discuss the second 224-gigabit Plugfest event at Keysight in Santa Clara, where over 20 companies test actual product interoperability across multiple vendors while nine compliance stations feed critical data back into IEEE specification development.

 ECL

Solving Power & Speed Challenges at 150-300kW Per Rack

Yuval Bachar, Co-founder and CEO of ECL, discusses how his AI data center company addresses the mismatch between rapid 9-12 month technology cycles and slower 18-24 month infrastructure timelines by building equipment and facilities on accelerated schedules while managing increasingly power-intensive racks reaching 150-300 kilowatts through a “flex grid” approach combining multiple energy sources.

 Lightmatter

Lightmatter's Optical Interconnects for AI Scale-Up

Steve Klinger, VP of Product at Lightmatter, presents the company’s optical scale-up interconnect solutions, featuring the Passage EVK50 system with DWDM technology that delivers 16 wavelengths per fiber with 400Gbps Tx and 400Gbps Rx. The architecture offers high energy efficiency and compact integration while aligning with the recently announced OCI MSA specifications using an 8-wavelength, 4+4 band interleave model.

 Synopsys

AI-Driven Multi-Die Design

Abhijeet Chakraborty, VP of Engineering at Synopsys, examines the accelerating adoption of multi-die designs for AI applications, highlighting how advanced packaging enables larger formats and better interconnect density while presenting complex engineering challenges in power delivery, thermal management, and multiphysics modeling that exceed the capabilities of traditional manual methods.

 UALink Consortium

UALink 2.0: Open GPU Interconnect for AI Clusters

Kurtis Bowman, Chairman of the UALink Consortium, presents announces the release of UALink 2.0 specifications featuring protocol-physical layer separation, in-network compute capabilities, enhanced manageability, and the consortium’s first UCIe-based chiplet specification, with the 115-member organization expecting customer solutions with 2.0 to be available in 2027.

 UCIe Consortium

UCIe Chiplet Connectivity for Performance & Efficiency

Debendra Das Sharma, UCIe Consortium Chair at UCIe Consortium, explains how UCIe technology enables chiplet connectivity within packages and package-to-package connections using co-packaged optics for composable systems with dynamic resource allocation. UCIe delivers bandwidth densities with 1-4 orders of magnitude improvement over PCIe and Ethernet.

 Xscape Photonics

Optical Compute Interconnect Standardization:

Vivek Raghunathan, Co-Founder and CEO of Xscape Photonics, discusses the Optical Compute Interconnect (OCI) standardization effort as an important milestone for scale-up interconnects and co-packaged optics implementations. He explains that OCI uses multi-color laser-based technology for GPU-to-memory communication and highlights Xscape’s recently announced laser module.

Director's Cut

 Arrcus

Unified Network Fabric for Distributed AI Workloads Across Data Centers

For our Director’s Cut, Sanjay Kumar, Vice President of Product Management and Marketing at Arrcus, and Keyur Patel, Founder and CTO at Arrcus, explain how Arrcus provides a unified network fabric that connects GPU resources across scale-up, scale-out, and scale-across layers for distributed AI workloads. They touch on how Arrcus differentiates itself through hardware-abstracted operating system software that works across multiple silicon platforms.

 DriveNets

Rethinking Data Center Network Architecture - Director's Cut

In this Director’s Cut video, Dudy Cohen, VP of Product Marketing at DriveNets, explains how Ethernet is evolving through advancing speeds (800G to 3.2T), co-packaged optics (CPO) integration, and scheduling layers that address lossiness to support AI infrastructure with GPU clusters scaling from 8,000 to nearly one million units. He describes how DriveNets has adapted its scheduled fabric technology, delivering superior time-to-first-token performance and lower cost-per-million-tokens compared to proprietary technologies.

 Marvell, AvidThink

Rethinking Data Center Network Architecture - Director's Cut

In an extended discussion, Rishi Chugh, VP and GM, Data Center Switching at Marvell, shares how AI workloads are driving data center networking, distinguishing between bandwidth-driven scale-out networking (800Gpbs to 1.6Tbps and beyond) and scale-up fabrics that unify xPU pods requiring memory sharing and ultra-low latency. Chugh shares Marvell’s commitment to deliver 100T and 200T scale-out fabrics along with 115T and 57T scale-up fabric devices supporting NVLink Fusion, UALink, and ESUN.

 Nokia

The Optics Scaling Challenge at 1.6T - Director's Cut

Neel Patel, GM, Optical Networking Component Solutions at Nokia, explains in our Director’s Cut edition, how AI data centers face significant power and supply challenges as GPU interconnect speeds double with each generation, requiring transitions from copper to optical solutions within racks. He highlights Nokia’s competitive advantages in indium phosphide (InP) technology, positioning the company as an open ecosystem component supplier for hyperscalers.

 Upscale AI

AI Data Centers Need Purpose-Built Networks - Director's Cut

In this director’s cut video, Aravind Srikumar, SVP of Product at Upscale AI, presents the company’s mission to deliver AI-specific networking solutions. Upscale AI differentiates through co-design optimization of ASIC, systems, and software to deliver deterministic performance for AI workloads while maintaining openness.

 Nokia

Integration is the Real Race in AI Data Center Networking

Mike Bushong, Vice President of Data Center at Nokia, discusses how AI workloads are fundamentally changing the data center networking market, with hyperscalers driving unprecedented demand and technical challenges while Nvidia’s GPU roadmap dictates industry development cycles. He explains that Nokia’s broad portfolio across switching silicon, DSPs, and differentiated optics from the Infinera acquisition positions the company to handle complex integration challenges, with deployment discussions beginning 18 months in advance and requiring sustainable commercial arrangements to support the staggeringly high capital expenditure requirements.