Technology · United States

Arista steps up its Ethernet challenge to Nvidia’s AI scale-up networking fabric

Credited to Network World · networkworld.com · 19h ago

Photograph · Network World

Arista sees Ethernet as the scale-up networking fabric of choice for AI and continues to build up its alternative to proprietary interconnects such as Nvidia’s NVLink technology. This week, Arista rolled out new Ethernet reference architectures as well as a raft of integration partnerships with industry players—including AMD, Arm, Broadcom, Meta, Microsoft, and Qualcomm—aimed at helping large enterprises and hyperscale customers make Ethernet-based scale-up and scale-out designs a reality.

Arista will offer its 7060EX7 Series switches, along with fiber patch panels, integrated liquid-cooling manifolds, drip trays with active leak detection, rack management controllers, and power shelves with backup battery units, and combine that with software and other technologies from partner vendors.

All are based on standard Ethernet technologies: “Scale-up based on ESUN Ethernet, and scale-out based on Ethernet with UEC features,” Hardev Singh, vice president and general manager of Arista’s cloud and AI group, told Network World.

“ Arista’s positioning contrasts with Nvidia proprietary NVLink and InfiniBand stacks,” Singh said.

The Ethernet for Scale-Up Networking (ESUN) initiative was formed by the nonprofit Open Compute Project and promises to advance the networking technology to handle scale-up connectivity across accelerated AI infrastructure. ESUN backers include AMD, Arista, ARM, Broadcom, Cisco, HPE Networking, Marvell, Meta, Microsoft, Nvidia, OpenAI and Oracle.

ESUN provides network operators flexibility across accelerators, switching silicon, optics, physical rack designs, and network software while retaining the high bandwidth, low latency, and reliable in-order delivery required for collective communication, according to Arista.

The Ultra Ethernet Consortium ( UEC ), meanwhile, includes AMD, Arista, Broadcom, Cisco, Eviden, HPE, Intel, Meta, and Microsoft. Its charter is to bring together industry leaders to build a complete Ethernet-based communication stack architecture for high-performance networking.

“Arista’s strategy is that, until recently, Nvidia was only game in town, and their proprietary scale-up was the primary technology being used to build scale-up networks,” Singh said. “As the scale-up TAM is increasing, and the non-Nvidia accelerator ecosystem is increasing, that opens up a brand new opportunity for Arista. And that’s the other differentiator here—that this is all Ethernet. Strictly Ethernet.”

“You just need to look at what’s going on in the AI networking arena,” Singh said, citing Microsoft Maia, Meta’s MTIA (Meta Training and Inference Accelerator), AMD’s Helios, and Google’s Tensor Processing Units (TPU). “These technologies are all opportunities for Ethernet players like Arista,” Singh said.

The designs that Arista and its partners will offer will give customers a path to integrate compute, networking, power, and liquid cooling across evolving high-density AI deployments, Singh said. The three primary designs of what Arista calls Etherlink SU-144, an Ethernet ESUN-based scale-up architecture, include:

“The cross-rack architecture is particularly noteworthy because it extends the scale-up domain beyond the physical limits of a single rack, supporting up to 1,024 accelerators while maintaining high-bandwidth, low-latency connectivity,” wrote Bob Laliberte, principal analyst and founder at Liberte Research Group, in a LinkedIn post about the news.

“Arista is also addressing a broader infrastructure shift. As AI clusters become denser and move toward liquid cooling, the rack is replacing the individual switch as the practical unit of deployment. Power distribution, thermal management, fiber density, leak detection and system validation must now be engineered as an integrated solution,” Laliberte wrote.

In terms of scale-out, Arista is defining UEC-based designs that include Multipath Reliable Connection (MRC) fabric resiliency, multi-plane routing, intelligent dynamic load balancing, and cluster load balancing to handle AI token generation efficiency and workloads.

“Customer designs vary by accelerator power, GPU count, bandwidth, thermal envelope, and rack configuration,” Singh said. “Arista capabilities in chassis engineering, thermal design, and signal integrity [are] positioned as differentiators.”

For example, Singh said one rack configuration features bandwidth that scales with next-generation interconnects. A 16-switch configuration approaches 1.6 petabytes per rack, and other designs with XPO, CPO, and NPO technologies optical could quadruple bandwidth, Singh said. “A Tomahawk 7, 200G-based generation could support 6.5 petabytes in one rack exceeding 100 kilowatts.”

“Compared to today’s OSFP-based switches, which deliver 1.6 Pb/s per rack, next-generation optics increases that to 6.5 Pb/s per rack. This massive densification translates to nearly a 50% reduction in the overall data center footprint, resulting in significant time and financial savings,” according to Singh.

“So, I want to make it clear, customers are not going to find a rack SKU on Arista’s price list. We are not selling integrated racks. We are selling the switches that go into those liquid-cooled switches that go into the rack,” Sing said. “We’re offering our software and technology for integration with custom joint development designs, and we’re partnering with these partners, VARs or system integrators that the customers can work with to deliver the fully integrated rack. In addition, we’re working with different optics vendors as well as partners like Foxconn, Quanta, and Hive,” Singh said.

The foundation of all these designs is Arista’s Network Diagnostics Infrastructure (NetDI) diagnostics and validation layer, which runs between Arista’s EOS operating systems or SONiC, FBOSS, or other NOS. Arista’s newer 1.6T AI switches, for example, integrate NetDI specifically to provide consistent diagnostics across different operating environments.

NetDI can track several switch diagnostics functions such as cable and optic health, device telemetry, troubleshooting and failure isolation, and other factors that help cloud companies manage these massive scale networks, Singh said.

“NetDI offers deep hardware-level validation, signal integrity analysis, secure boot attestation, and Single Event Upset (SEU) resiliency, supporting rich telemetry for the switch, physical optics, power shelves, and liquid cooling infrastructure,” Arista wrote in a blog about the news.

TechnologyUnited States

Liquid AI builds personal AI around device-level context

Credited to SiliconANGLE · siliconangle.com55m ago

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On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment.

Model builders are rethinking architectures designed around elastic cloud capacity. The edge offers fixed hardware but a far richer view of the user, which makes it the natural home for personal AI, according to Jeffrey Li (pictured), chief operating officer of Liquid AI Inc.

“The vision we have is that we should bring AI … closer to the user,” he said. “So we focus on building these AIs to run on the devices all around us.”

Li spoke with theCUBE Research’s Dave Vellante and John Furrier at the Fully Connected event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed on-device AI agents, fixed edge compute and observability loops. (* Disclosure below.)

Personal AI needs context within device limits

Liquid AI’s Liquid Context, which is optimized for Snapdragon processors, sits between models, agents and hardware. It uses device signals to build an understanding of who the user is and what they’re trying to accomplish, Li explained.

“What is the best form factor to capture that signal? It’s the devices in our pockets,” Li said. “It’s our phones, it’s our wearables, it’s our watches, it’s our PCs, it’s our cars.”

Agent harnesses, the software that turns models into functioning agents, also manage user context. Keeping that context in an ever-growing text file poses challenges on devices with limited resources, according to Li. Liquid AI uses its own models to decide which information to retain and how to compress it.

“The problem with devices is that you have fixed compute,” he said. “You have to fit within the zero-sum compute. That means a lot of the assumptions around how harnesses today are built no longer hold at the edge.”

The company’s work includes a collaboration with Mercedes-Benz Group AG to bring on-device AI to its cars. Keeping those agents aligned with user expectations long after deployment is the next step, Li noted.

“From here, we want to build these systems and these agents to be able to self-heal and improve and personalize on their own over time,” he said. “We’re building observability loops and continuous improvement loops that will improve both the model and the harness over time through natural usage.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Fully Connected event:

(* Disclosure: TheCUBE is a paid media partner for the Fully Connected event. Neither CoreWeave, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Technology · United States

Breaking the AI Infrastructure Power Wall: From Grid to xPU

Credited to EE Times · eetimes.com · 5h ago

Photograph · EE Times

Navitas 2.0 Completes Power Portfolio with Claros Acquisition

Time: 8:00am PDT | 11:00am EDT 17:00 CEST

AI infrastructure is scaling rapidly, driving higher rack power, compute density, and xPU power demands. These trends are exposing the limits of traditional power architectures and creating a new AI infrastructure “power wall.”

Navitas 2.0 marks a strategic shift toward high-power, high-growth markets, including AI data centers, energy and grid infrastructure, performance computing, and industrial electrification. Its portfolio of GaN, high-voltage and ultra-high-voltage SiC, and integrated voltage regulator (IVR) technologies addresses critical power conversion and delivery challenges across the AI power chain.

Join this webinar where our panel will highlight Navitas’ acquisition of Claros and how its IVR technology extends the Navitas portfolio to the final stage of power delivery: the xPU. By moving power conversion closer to the processor through Vertical Power Delivery (VPD) and Embedded Power Delivery architectures, IVRs reduce losses and impedance while enabling faster transient response and higher power density.