EdgeIR.com - Digital Infrastructure News

Lambda doubles down on NVIDIA stack with 10,000+ Blackwell GPUs and CPO networking push

Stephen Mayhew — Fri, 10 Apr 2026 10:00:12 +0000

Lambda recently announced it’s becoming a launch partner for the NVIDIA Vera CPU platform and NVIDIA STX. The GPU-native AI infrastructure provider will deploy NVIDIA Quantum-X800 infiniBand photonics co-packaged optics in an AI factory with 10,000+ NVIDIA Blackwell Ultra GPUs. Lambda's bare metal instances made its way out of the lab and into the core cloud offering, giving users direct access to hardware while avoiding virtualization overhead for distributed AI training workloads. Designed for launching thousands of parallel AI environments, the NVIDIA Vera CPU platform enables maximally high memory bandwidth which optimizes reinforcement learning and agentic AI workloads. The NVIDIA STX is a modular architecture for AI storage that augments inference, analytics, and training with next-gen hardware optimized KV-cache management. Co-Packaged Optics (CPO) networking enables faster, cost-efficient AI infrastructure suitable for large-scale AI factories, alleviating major efficiency bottlenecks found in current approaches. “The race to build AI factories isn’t won on GPU counts alone,” says Dave Salvator, director of accelerated computing at NVIDIA. “Network architecture is what determines whether those systems can perform at scale. Getting this right is what allows AI infrastructure to power services used by hundreds of millions of people around the world.“ Lambda oversees one of the largest deployments of NVIDIA Quantum-X800 CPO switches, highlighting how critical network architecture is when scaling AI systems. These announcements further bolster Lambda's AI infrastructure platform, which empowers frontier labs, enterprises, and hyperscalers with proven and energy-efficient workhorses built for reliability at scale. Lambda continues its mission to make AI compute ubiquitous, leveraging a decade-long collaboration with NVIDIA to advance its Superintelligence Cloud platform.

DDN and Zadara target sovereign AI deployments with multi-tenant NVIDIA factory stack

Stephen Mayhew — Thu, 09 Apr 2026 10:00:08 +0000

DDN and Zadara announced a partnership to deliver high-performance AI infrastructure for sovereign clouds and multi-tenant AI factories, leveraging NVIDIA reference designs. The integration combines the DDN EXAScaler AI data platform with Zadara’s cloud-native, AI-optimized infrastructure for scalable, secure, and efficient AI deployments. The solution solves critical problems of enterprise AI such as real-time infrastructure complexity, GPU performance, compliance and multi-tenant governance that hinder faster deployment of AI. “NVIDIA reference designs are accelerating the broad adoption of AI factories. Yet organizations need a cloud-native platform that can operationalize them simply and efficiently in real-world, multi-tenant environments while complying with data sovereignty regulations,” says Yoram Novick, CEO at Zadara. “DDN EXAScaler brings the high-performance AI data foundation needed to meet demanding sovereign and enterprise requirements while Zadara delivers the orchestration, isolation, and policy control required to deploy AI infrastructure quickly, securely, and efficiently.” Zadara’s platform decouples AI infrastructure operations with performance, compliance and tenant isolation in accordance with NVIDIA best practices. This partnership powers NVIDIA-enabled AI factories for secure multi-tenancy, predictable performance and policy-based orchestration with rapid time to import. The integrated solution enables sovereign AI and multi-tenant environments with high throughput, GPU-aware scheduling, and compliance controls. This initiative aims to make enterprise AI more accessible, affordable and practical by minimizing operational complexity while improving time-to-value. The partnership between DDN and Zadara reinforces the need for AI factories as a cornerstone of enterprise, telcos, and service providers.

Premio targets multi-camera edge AI with new Jetson Orin systems

Stephen Mayhew — Wed, 08 Apr 2026 10:00:45 +0000

Rugged edge AI and embedded computing provider Premio debuted two new models to its JCO-Series of rugged NVIDIA Jetson Orin edge AI computers at ISC West 2026. Designed for next-gen vision AI applications, these models power multi-camera deployments used in multiple industries including security, transportation and smart infrastructure by supporting up to four GMSL2 cameras. “As AI-driven vision systems continue to transform industries such as security, transportation, and smart infrastructure, organizations need reliable computing platforms capable of processing data directly at the edge," says Dustin Seetoo, VP of product marketing at Premio. “With the JCO-1000-ORN-B and JCO-1000-ORN-C, we are expanding the JCO-1000-ORN Series to support multi-camera AI deployments while maintaining the rugged performance required for real-world edge environments.” These supercomputers come equipped with NVIDIA Jetson Orin NX/Nano modules, which provide up to 157 TOPS of AI performance and ruggedized I/O for edge environments. Highlights will include dual LAN or rugged M12 LAN connectors, high-speed NVMe M.2 storage, 4G/5G and Wi-Fi support including CAN Bus and extended operating temperature range (-20°C to 55°C). The systems are small form factor, vehicle-ready with 9–36VDC input and certified to CE, FCC and UL global standards. The new models will be available for order by mid-Q2 2026.

Hosted.ai raises $19M to tackle GPU underutilization and reshape AI infrastructure economics

Stephen Mayhew — Tue, 07 Apr 2026 22:42:11 +0000

AI infrastructure startup hosted·ai secured $19M in seed funding to simplify and optimize GPU infrastructure for AI, led by Creandum with participation from other investors like Repeat VC and People Ventures. The startup focuses on the inefficiencies of GPU infrastructure, including high costs, low utilization — with average usage at just 40% — and limited access to regional suppliers. hosted·ai's software stack includes: hosted·ai: A GPUaaS platform improving GPU utilization by up to 5x, reducing costs, and enabling resource sharing. packet·ai: A neocloud service leveraging optimized GPU infrastructure for competitive pricing. GPUaaS.com: A matchmaking service connecting enterprises with GPU providers for scalable solutions. The company will target the “GPU waste” problem and allow regional service providers a piece of this AI infrastructure pie. “The GPU market has a waste problem, not a scarcity problem,” says Ditlev Bredahl, CEO of Hosted.ai. “We’ve spent 25 years building infrastructure software that makes service providers competitive and the GPU opportunity is the biggest we’ve seen. This funding lets us move faster: more platform, more partners, more regions. We’re building the operating system for the GPU economy, and this round puts us in a strong position to do exactly that.” Some of the founding team members have previous experience working with infrastructure technologies at VMware, NVIDIA or XenSource. Founded in 2024 and launched in 2025, hosted·ai is a global company offering products across the US, EMEA and Asia-Pacific. Their mission is to revolutionize the economics of GPU computing so that AI infrastructure becomes affordable and accessible for developers and companies alike.

NVIDIA and T-Mobile push AI-RAN to turn 5G networks into distributed edge compute platforms

Stephen Mayhew — Tue, 07 Apr 2026 10:00:23 +0000

NVIDIA and T-Mobile are working with Nokia and a growing ecosystem of developers to deploy vision AI applications over distributed edge networks, using AI-RAN infrastructure and the NVIDIA Metropolis platform to turn wireless networks into platforms for edge AI computing. T-Mobile works with NVIDIA and has started to pilot AI-RAN infrastructure to enable edge AI workloads distributed across the network while maintaining robust 5G connectivity. NVIDIA Metropolis VSS 3 Blueprint accelerates reasoning video analytics through modular architecture, multimodal understanding and agentic search capabilities. “Turning networks into distributed AI computing platforms to unlock the full potential of physical AI will require ultra-low latency and space time coherency at the network edge for billions of endpoints, and that's what we've built at T-Mobile,” says Srini Gopalan, chief executive officer of T-Mobile. “With the first nationwide 5G Standalone and 5G Advanced network, we are uniquely positioned to help power a future where intelligent systems don’t wait on the cloud but rely on intelligent networks that allow them to act in real time.” Some of the use cases are smart city operations, automated inspections for utilities, vision-based facility management and real-time industrial safety. T-Mobile’s full 5G standalone radio access network (RAN) enables low-latency, secure and wide-area connectivity, a requirement to scale physical AI applications. Leveraging NVIDIA's infrastructure, developers such as Siemens Energy, Levatas and Fogsphere are building AI agents capable of real-time action and predictive maintenance. Together, this collaboration aims to turn 5G networks into distributed AI platforms that enable billions of interacting devices in real time. T-Mobile also recently tapped Red Hat OpenShift to streamline edge and 5G cloud operations.

Nscale moves into power with AIPCorp deal, building 8GW U.S. AI campus to bypass energy bottlenecks

Stephen Mayhew — Mon, 06 Apr 2026 06:00:28 +0000

UK-based neocloud Nscale recently secured the largest Series C round in European history, raising $2 billion. Nscale then made good on its full-stack AI hyperscaler vision by acquiring two key assets: American Intelligence & Power Corporation (AIPCorp) and the Monarch Compute Campus in West Virginia. Monarch Compute Campus will be the first state-certified AI microgrid in the U.S. It is scalable up to 8 gigawatts of clean, renewable power by 2031. ”Nscale is a global company, and the US is the world's largest AI infrastructure market. AI infrastructure needs to be built where demand is, and right now a significant share of that demand is in the United States,” says Josh Payne, CEO of Nscale. “Monarch allows us to meet that demand. The acquisition builds on our existing US footprint and reflects the pace at which we are scaling to serve customers around the world." It will initially have 2 gigawatts of power capacity to be operational by 2028 to support AI infrastructure growth in the U.S. To manage the integration of energy and compute, Nscale created a new division based in Houston, Texas - Nscale Energy & Power. Nscale will be joined by the leadership and staff of AIPCorp, which has nearly a decade of experience building AI infrastructure, as it develops a global presence. The reason behind the acquisition is an attempt to resolve bottlenecks in AI compute demand with a vertically integrated power-and-compute model. Nscale specializes in scalable AI infrastructure across Europe and North America for all aspects of the AI life-cycle such as training, fine-tuning, and inference.

Zededa and Submer target off-grid AI with modular, liquid-cooled edge GPU systems

Stephen Mayhew — Fri, 03 Apr 2026 10:00:49 +0000

Zededa and Submer have partnered to deliver modular, liquid-cooled edge AI infrastructure for high-density GPU inference in locations without traditional data centers. Linking Submer’s liquid-cooled AI infrastructure with Zededa’s edge intelligence platform, the partners will deliver scalable, secure and resilient edge AI deployments. “AI is rapidly moving from centralized cloud environments into real-world operations, from industrial sites to telecom networks and remote energy infrastructure,” says Patrick Smets, CEO of Submer. “Delivering that intelligence requires purpose-built AI infrastructure that operates efficiently in environments where traditional data centers simply cannot exist. By combining Submer’s liquid-cooled high-density AI infrastructure with Zededa’s edge intelligence platform, we’re enabling organizations to deploy scalable, resilient AI infrastructure anywhere it is needed.” Three modular solutions are offered: edge pods (from 2 to 8 GPUs); ruggedized micro-data centers (to 168 GPUs), and; megawatt-scale containerized systems (up to 800 GPUs). Submer’s liquid cooling technology improves energy efficiency, reduces water consumption and enables high-density GPUs in harsh environments. Zededa’s software-defined resilience reduces hardware redundancy costs by redistributing workloads during node failures, ensuring uptime and lowering total cost of ownership. The deployments address a wide range of AI workloads such as real-time computer vision, predictive maintenance and industrial automation so that AI can run at the edge in remote or extreme environments. The first pilot deployments are expected to begin with industrial and telecommunications customers later this year. The partners will focus on delivering the emerging edge AI infrastructure necessary to bring workloads from centralized cloud environments to operational and industrial settings. Late last year Zededa rolled out full-stack edge Kubernetes to tackle large-scale AI deployments.

Crusoe rolls out edge zones as neocloud race shifts toward distributed AI infrastructure

Stephen Mayhew — Thu, 02 Apr 2026 10:00:54 +0000

Neocloud provider Crusoe recently announced modular AI data centers that provide low-latency, sovereign and rapidly deployable AI infrastructure at the edge. These edge zones deliver AI compute capabilities in location-specific areas based on geography, and the use cases, not supported by traditional hyperscale providers. “Crusoe Edge Zones powered by Crusoe Spark represent the continued expansion of our vertically integrated ‘AI Factory’ vision," says Cully Cavness, co-founder, president, and chief strategy officer of Crusoe. “By optimizing these modular AI factories to run both the Crusoe Cloud platform and our Managed Inference product, we are delivering a high-performance, distributed solution that provides the speed, sovereignty, and quality that the next generation of AI requires.” Crusoe can quickly deploy new cloud zones, within three months in some instances, expanding cost-saving AI capacity due to its vertically integrated approach. The main use cases are low-latency inference, dedicated enterprise clusters in multiple locations and sovereign AI deployments to serve regulated industries and governments. Crusoe envisions large-scale campuses for model training overhead and modular distributed compute for high-performance edge delivery. The company focuses on driving energy efficient, scalable AI infrastructure in its mission to accelerate energy and intelligence abundance. Organizations can reach out to Crusoe for deployment opportunities if they have geographic expansion, low-latency, or sovereign infrastructure requirements.

Meta locks in up to $27B AI capacity deal with Nebius, signaling neocloud scale-up

Stephen Mayhew — Wed, 01 Apr 2026 09:00:14 +0000

Amsterdam based neocloud Nebius signed a five-year AI infrastructure deal with hyperscaler Meta, valued at up to $27 billion. Starting in early 2027, Nebius will deliver dedicated NVIDIA Vera Rubin-based AI capacity with a total commitment of $12 billion. Nebius is a unified cloud solution for AI, providing end-to-end tools for the development and deployment of AI. “We are pleased to expand our significant partnership with Meta as part of securing more large, long-term capacity contracts to accelerate the build-out and growth of our core AI cloud business,” says Arkady Volozh, founder and CEO of Nebius. Meta committed to purchasing additional compute capacity worth up to $15 billion over the next five years. This deal will accelerate the expansion of Nebius's AI cloud product line and deepen its partnership with Meta. Nebius plans to resell its excess capacity to third-party customers with Meta purchasing all unutilized capacity. Meta previously purchased capacity from Nebius worth $3 billion that went live in February, 2026. Nebius's 2026 financial guidance is unchanged under the new agreement. Nvidia also doubled down on neoclouds with a recent $2B investment in Nebius.

Cisco and NVIDIA push secure AI Infrastructure from core data centers to the edge

Stephen Mayhew — Tue, 31 Mar 2026 09:00:41 +0000

Cisco and NVIDIA have expanded their Secure AI Factory to enable AI deployment across central data centers and local edge sites, ensuring real-time decision-making and enhanced security. The collaboration introduces NVIDIA Spectrum-X switch silicon and Cisco operating systems for versatile AI infrastructure building blocks for businesses. "Most organizations understand the potential for AI to transform their businesses, but they're navigating how to deploy the technology safely and at scale," says Chuck Robbins, chair and CEO, Cisco. "In partnership with NVIDIA , we're solving that challenge with an architecture that sets a new standard for performance, making it simpler to deploy, operate, and secure AI infrastructure." Cisco has also enhanced security by building features to provide Hybrid Mesh Firewall policies and leveraging Cisco AI Defense in order to secure AI systems and multi-agent actions. The partnership advances NVIDIA’s OpenShell platform, which brings additional security controls and guardrails to AI agent actions and workflows. Cisco’s AI offerings now support edge inferencing and are designed to run AI workloads closer to data sources like hospitals, warehouses and vehicles. The high-performance hardware, including Cisco N9100 switches with NVIDIA Spectrum-6, provides scalability and efficient infrastructure for AI. Cisco AI Defense secures the deployment of AI agents by checking and validating their activity, providing a bridge between innovation and risk. Industry experts commend the collaboration for streamlining AI implementation, enhancing scalability, and tackling security issues in real-time AI applications. Andrew Leece, COO and founder, Sharon AI, says, "With NCP RA compliance and the 51.2T Spectrum-4 based N9100 switch availability, we will be scaling our AI infrastructure with robust performance and efficiency. The G300 Silicon One-based N9300 switches provide the flexibility to meet evolving customer needs." Cisco and Sharon AI, jointly with NVIDIA, recently opened Australia’s first Cisco Secure AI Factory for secure, high-performance AI infrastructure.