2026 Showcase
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!
Thanks to our sponsors for making the report possible!
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!
Thanks to our sponsors for making the report possible!
Video interviews
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.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.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.Ethernet for AI Networking at Scale: Building 100K+ XPU Clusters
Hasan Siraj, VP of Product Management for the Core Switching Group at Broadcom, explains how Ethernet has become the industry standard for AI networking at scale, enabling clusters of over 100,000 XPUs through scale-up, scale-out, and scale-across architectures. He discuses Broadcom's solution portfolio including: Tomahawk Ultra, Tomahawk 6, Thor Ultra and Jericho 4.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.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.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.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.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. They explain how Arrcus addresses the needs of telcos, colocation providers, hyperscalers, enterprises, and neoclouds by providing a unified fabric that is scalable, highly secured, fully programmatic, and orchestrated on demand across different data centers.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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. 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.NVIDIA's AI Factory Networking Stack
Gilad Shainer, SVP of Networking at NVIDIA, shares why building AI factories requires designing four distinct infrastructure layers—scale-up using NVLink, scale-out using InfiniBand or Spectrum X Ethernet, scale-across using Spectrum XGS, and storage with NVIDIA Bluefield-4 STX architecture.Featured companies
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