# data centers Physical facilities that house computing infrastructure for running applications, processing and storing data, and delivering services. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers DevFeed: [Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers]() Original publisher: [Read original article]() Author: Josh Parker Published: 2026-09-16T13:00:33Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [data centers](), [AI Infrastructure](), [Google](), [Nvidia](), [Artificial Intelligence](), [datacenter](), [benchmarking](), [Requirements]() Tags: [ai](), [ai-data-centers](), [ai-infrastructure](), [capacity](), [corporate](), [data-centers](), [energy](), [google](), [infrastructure](), [launch](), [metrics](), [nvidia](), [performance-metrics](), [requirements](), [resource](), [systems]() ### AI overview Emerald AI, Google and NVIDIA announced the AI Energy Management Alliance, a coalition focused on flexible AI data centers that can dynamically adjust electricity use in response to grid conditions. The article describes technology-neutral, performance-based requirements covering response speed, duration, predictability and emergency behavior. ### Source excerpt AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center. Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use [...] ## Dropbox Outlines How Focusing on Existing Infrastructure Efficiency Can Create Headroom for AI DevFeed: [Dropbox Outlines How Focusing on Existing Infrastructure Efficiency Can Create Headroom for AI]() Original publisher: [Read original article]() Author: Matt Foster Published: 2026-09-16T07:15:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [cloud-infrastructure](), [Magic Pocket](), [data centers](), [networking]() Tags: [ai](), [ai-architecture](), [ai-ml-data-engineering](), [capacity](), [cloud](), [data-center](), [data-storage](), [devops](), [dropbox](), [dropbox-datacenter](), [efficiency](), [infrastructure](), [infrastructure-optimisation](), [magic-pocket](), [ml-data-engineering](), [networking](), [news](), [optimization](), [storage](), [sustainable-computing]() ### AI overview Dropbox describes how long-running infrastructure optimization helps it accommodate growing AI demand by improving forecasting, fleet utilization, storage density, hardware lifecycles, and rack-level power delivery. Its storage infrastructure has used more than 50% less power per petabyte since 2020. ### Source excerpt Dropbox has outlined how a decade of infrastructure optimization is helping it absorb growing demand from AI without treating new data-center capacity as the only answer. Its work spans forecasting, fleet utilization, storage density, hardware lifecycles, and rack-level power delivery, much of it predating the current AI boom. By Matt Foster ## AI, JD, and other letters of the law DevFeed: [AI, JD, and other letters of the law]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-09-15T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [data centers]() Tags: [agentic-ai](), [ai](), [big-data](), [data-centers](), [law](), [podcast](), [policy](), [se-stackoverflow](), [se-tech](), [university]() ### AI overview Ryan chats with Kevin Frazier about the legal and social impacts of data centers, workforce disruption, and regulating AI for child safety through existing consumer protection laws. ### Source excerpt Ryan chats with Kevin Frazier, director of the AI Innovation and Law program at the University of Texas School of Law, about the legal and social impacts of data centers, the realities of workforce disruption, and regulating AI for child safety using existing consumer protection laws. ## Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November DevFeed: [Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-14T18:03:44Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Inference](), [AI Infrastructure](), [Inference Performance](), [cpu](), [data centers](), [Confidential Computing](), [Arm](), [Hardware](), [NVLink](), [Nvidia]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [arm](), [benchmark](), [data-centers](), [enterprise](), [fujitsu](), [hardware](), [nvidia](), [nvlink]() ### AI overview Fujitsu is introducing MONAKA Servers built around its 2nm FUJITSU-MONAKA processor for AI inference in air-cooled data centers. The servers offer up to 144 CPU cores, matrix instructions, SVE2 vector processing, hardware-level confidential computing, and planned NVLink Fusion integration with NVIDIA GPUs. Fujitsu claims higher inference throughput and reduced cooling power consumption, but the article notes that supporting benchmark details are unavailable. ### Source excerpt Fujitsu is bringing its 2nm FUJITSU-MONAKA processor to AI infrastructure with a new server family designed to run AI inference in air-cooled data centers without requiring specialized liquid cooling. The MONAKA Server is designed, developed, and manufactured in Japan, with component and manufacturing traceability for sovereign AI deployments. The first MONAKA Servers will come in The post Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November appeared first on StorageReview.com. ## Powering the AI era: How wave energy can complement a 24/7 energy mix DevFeed: [Powering the AI era: How wave energy can complement a 24/7 energy mix]() Original publisher: [Read original article]() Author: Elias Habbar-Baylac Published: 2026-09-10T15:20:22Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [AI Infrastructure](), [data centers](), [datacenter](), [Artificial Intelligence]() Tags: [ai](), [ai-infrastructure](), [chief-sustainability-office](), [cisco-purpose](), [data-center](), [data-centers](), [energy](), [environmental-sustainability](), [generation](), [infrastructure](), [megawatt](), [our-corporate-purpose](), [sustainability]() ### AI overview The article explains how wave energy could complement solar, wind, and batteries in meeting the continuous electricity needs of AI-era data centers. It discusses a modeled 100 MW flat-load data center and energy portfolios evaluated for cost, reliability, emissions, and round-the-clock availability. ### Source excerpt CorPower Ocean, a Cisco Investments portfolio company, has explored how wave energy technology could complement other sources for 24/7 energy needs. ## Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects DevFeed: [Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-08T17:17:46Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [AI Infrastructure](), [Amazon Web Services](), [data centers](), [Chip design](), [networking](), [Amazon Bedrock](), [amazon]() Tags: [ai](), [ai-data-centers](), [ai-inference](), [ai-infrastructure](), [amazon-bedrock](), [aws](), [chip-design](), [cost](), [data-centers](), [dsp](), [efficiency](), [enterprise](), [networking](), [partnership](), [qualcomm]() ### AI overview Qualcomm Technologies and Amazon are collaborating across multiple generations to develop custom silicon for AWS AI data centers, primarily targeting AI inference. The agreement also covers 1.6T optical connectivity for data center networks and Qualcomm's use of AWS infrastructure, including Amazon Bedrock, for electronic design automation workloads. ### Source excerpt Qualcomm Technologies and Amazon have entered into a multi-generation collaboration to deliver customized silicon at scale for AWS's AI data centers, with AI inference as the primary target. The agreement pairs Qualcomm's power-efficient processing, silicon design, and system-level integration with Amazon's AI infrastructure, and is aimed at the compute, memory bandwidth, networking, and energy constraints The post Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects appeared first on StorageReview.com. ## Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data DevFeed: [Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-03T16:22:15Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Inference](), [Inference Performance](), [AI Infrastructure](), [model-serving](), [Open Source Models & Datasets](), [data centers](), [networking]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [architectures](), [data-centers](), [enterprise](), [hardware](), [inference](), [latency](), [networking](), [open-source](), [production]() ### AI overview Equinix Inference Exchange is a distributed AI inference platform that places NVIDIA compute and Together AI's open-model serving closer to enterprise data, users, and applications. It combines Equinix's interconnection infrastructure, NVIDIA hardware, and support for more than 200 open-source models to address latency, data sovereignty, networking complexity, and inference costs. ### Source excerpt Equinix has expanded its partnership with NVIDIA and entered a new collaboration with Together AI to launch Equinix Inference Exchange. Designed as a distributed AI inference architecture for enterprise deployments, the platform aims to shift compute workloads closer to core data repositories, end users, and operational applications. Announced alongside Equinix Fabric One at the Equinix The post Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data appeared first on StorageReview.com. ## LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate DevFeed: [LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T15:22:26Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data](), [Disk image](), [Resilience](), [data centers](), [Artificial Intelligence](), [ibm]() Tags: [2026](), [ai](), [architectures](), [data](), [data-center](), [datasets](), [enterprise](), [enterprise-storage](), [hardware](), [ibm](), [report](), [resilience](), [retention](), [storage](), [training](), [upgrade]() ### AI overview LTO tape capacity shipments rose 57% year over year in Q1 2026, driven by continued LTO-9 adoption, the rollout of LTO-10, and growing enterprise data and archival needs. The article explains how tape supports economical cold storage, long-term retention, AI training datasets, and data center power constraints. ### Source excerpt The Linear Tape-Open (LTO) Program Technology Provider Companies, comprising Hewlett Packard Enterprise, IBM Corporation, and Quantum Corporation, have released their annual tape media shipment report. Following sustained enterprise data expansion, the report highlights a strong start to 2026, driven by continued LTO-9 adoption and the rollout and initial capacity ramp-up of LTO-10 media. According to The post LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate appeared first on StorageReview.com. ## AI data centers: the five hard problems money cannot buy away DevFeed: [AI data centers: the five hard problems money cannot buy away]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-30T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [Artificial Intelligence](), [data centers](), [Hardware](), [systems]() Tags: [ai](), [capacity](), [compute](), [data-centers](), [datacenters](), [hardware](), [heat](), [infrastructure](), [power](), [reliability]() ### AI overview The article argues that AI data-center expansion is constrained by the simultaneous need to secure power, grid connections, cooling and water strategies, equipment, skilled workers, permits, and productive compute. It discusses five industry challenges, with the supplied text covering power constraints and the physical and water implications of liquid cooling. ### Source excerpt The AI buildout is not mainly a real-estate problem. It is a race to integrate power, cooling, equipment, permits, and useful compute at the same time. ## Worth Reading 082926 DevFeed: [Worth Reading 082926]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-29T14:47:35Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [cdnjs](), [ChatGPT](), [genai](), [data centers](), [DNSSEC]() Tags: [chatgpt](), [data-centers](), [dnssec](), [genai](), [technology](), [worth-reading]() ### AI overview A developer-oriented reading roundup covering content delivery networks, ChatGPT and scaling toward artificial general intelligence, high-density data-center builds, GenAI-assisted medical image analysis, and the practical difficulty of maintaining DNSSEC. ### Source excerpt The term "content delivery network" reflects the technology's original value proposition. Sam Altman released ChatGPT for free in late 2022. Many users fell in love both with it and Altman's argument that artificial general intelligence could be achieved through scaling. T If you work around data centers and high-density builds, you have probably heard the term "Multi-Core Fiber" thrown around. Ask yourself these questions, assuming you have a serious medical condition and your doctors are going to be using GenAI to scan your images and test results to discover the breadth and depth of your condition and to recommend treatment. Maybe adoption is low not because people don't believe DNSSEC is useful, but because turning it on and keeping it on is still genuinely harder than it should be. ## MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet DevFeed: [MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet]() Original publisher: [Read original article]() Author: Arvind Srinivasan; Neil Spring; Omar Baldonado; Rajiv Krishnamurthy Published: 2026-08-24T18:02:29Z Content type: article Language: en Sources: [Engineering at Meta]() Topics: [Ethernet](), [Networks](), [AI Infrastructure](), [GPU](), [data centers](), [Low Latency](), [telemetry](), [Inference]() Tags: [ai-infrastructure](), [data-center-engineering](), [data-centers](), [data-infrastructure](), [ethernet](), [gpu](), [low-latency](), [networking-traffic](), [open-source](), [performance](), [real-time](), [telemetry]() ### AI overview Meta introduces MetaRoCE, an RDMA transport protocol designed for AI workloads on commodity Ethernet at million-GPU scale. The article describes its release through the Open Compute Project and explains how endpoint intelligence, packet spraying, fine-grained logical paths, and real-time telemetry aim to provide high throughput, low tail latency, and operational simplicity for distributed training and inference. ### Source excerpt Training and serving frontier AI models depends on fast, reliable networks that move data between GPUs without wasting compute cycles. To meet this challenge at scale, Meta designed MetaRoCE - a clean-sheet RDMA transport protocol purpose-built for AI workloads on commodity Ethernet. We're releasing the MetaRoCE specification, a reference software implementation and a compliance test [...] Read More... The post MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet appeared first on Engineering at Meta. ## Improving infrastructure efficiency for growing demand in the age of AI DevFeed: [Improving infrastructure efficiency for growing demand in the age of AI]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev Published: 2026-08-18T17:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [cloud-infrastructure](), [systems](), [data centers](), [Optimization](), [Hardware](), [Artificial Intelligence](), [networking](), [Magic Pocket]() Tags: [ai](), [availability](), [data-center](), [efficiency](), [energy](), [hardware](), [industry](), [infrastructure](), [networking](), [optimization](), [storage](), [systems]() ### AI overview Dropbox describes a system-level approach to improving infrastructure efficiency as AI-driven demand grows. Its engineering teams optimize capacity planning, hardware, power, cooling, storage, servers, networking, and facility design together so existing infrastructure can support growth before new data center capacity is added. ### Source excerpt As demand for AI continues to grow, so does the infrastructure needed to support it. ## Multi-Core Fiber and How It Is Used in Data Centers DevFeed: [Multi-Core Fiber and How It Is Used in Data Centers]() Original publisher: [Read original article]() Author: j2sw Published: 2026-08-12T14:27:20Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [data centers](), [datacenter]() Tags: [400g](), [cabling](), [connectors](), [data-center](), [data-centers](), [fiber](), [fiber-optics](), [it-operations](), [mpo](), [mtp](), [multi-core-fiber](), [network-engineering-resources](), [optics](), [panel](), [parallel](), [single-mode](), [structured-cabling]() ### AI overview This article explains how "multi-core fiber" is used in data center discussions, distinguishing multi-fiber or high-fiber-count cables from true multicore fiber. It covers cable construction, fiber counts, single-mode and multimode options, and MPO and MTP connectors. ### Source excerpt If you work around data centers and high-density builds, you have probably heard the term "Multi-Core Fiber" thrown around. People use it in a few different ways. Sometimes, it means a new fiber trunk that comes into a cabinet as a single cable and breaks out into dozens or even hundreds of fibers. That setup ... Read more The post Multi-Core Fiber and How It Is Used in Data Centers appeared first on Justin Wilson (j2sw). ## In-region inference, open models, and new European infrastructure for sovereign AI. DevFeed: [In-region inference, open models, and new European infrastructure for sovereign AI.]() Original publisher: [Read original article]() Published: 2026-08-11T12:00:27Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Inference](), [AI Infrastructure](), [AI Models](), [data centers](), [Cloud](), [Latency]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [cloud](), [compliance](), [compute](), [data](), [data-centers](), [europe](), [inference](), [infrastructure](), [latency](), [models](), [open](), [production](), [scale]() ### AI overview Mistral outlines a sovereign AI infrastructure strategy centered on regional inference, open-model access, and long-term European compute capacity. Regional Endpoints let customers select European or US processing regions, while a Priority Tier offers committed service levels for mission-critical workloads. ### Source excerpt Mistral is bringing together the inference infrastructure, open models, and long-term commitments Europe needs to control its AI future, and setting a roadmap for the world. ## The Database at 550 Kilometers: What Orbital Computing Means for Distributed Databases DevFeed: [The Database at 550 Kilometers: What Orbital Computing Means for Distributed Databases]() Original publisher: [Read original article]() Author: Isaac Wong Published: 2026-08-06T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [data centers](), [Databases](), [Artificial Intelligence](), [Inference](), [GPU]() Tags: [2026](), [ai](), [compute](), [data-center](), [data-centers](), [database](), [distributed](), [energy](), [gpu](), [inference](), [infrastructure](), [nvidia](), [solar](), [space](), [spacex]() ### AI overview The article examines orbital data centers as a possible response to the energy, cooling, and land constraints of terrestrial facilities. It describes proposed satellite-based computing projects, including an orbital Nvidia H100 Gemini inference workload, and outlines the roles of solar power, radiative cooling, and open orbit. ### Source excerpt In Ashburn, Virginia, a row of servers draws 40 megawatts from the grid and exhales it as heat. ## Kimi K3 and Kimi K3 Fast with ZDR and US-based providers now on AI Gateway DevFeed: [Kimi K3 and Kimi K3 Fast with ZDR and US-based providers now on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-27T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [Inference](), [Latency](), [data centers](), [Vercel](), [baseten]() Tags: [ai](), [ai-gateway](), [baseten](), [coding-agents](), [compliance](), [data-centers](), [inference](), [latency](), [retention](), [sdk](), [us](), [vercel]() ### AI overview Vercel's AI Gateway now supports Moonshot AI's Kimi K3 and Kimi K3 Fast through US-based providers including Baseten and Fireworks. The release adds Zero Data Retention, US-only inference routing, provider failover, model endpoint details, coding-agent setup, and playground access. Kimi K3 Fast offers lower latency at a higher per-token cost. ### Source excerpt Kimi K3 from Moonshot AI and its faster serving path, Kimi K3 Fast, are now available from US-based providers on AI Gateway, including Baseten and Fireworks. Zero Data Retention (ZDR) is also supported for both models. Running Kimi K3 on US-based providers lets teams with data residency and compliance requirements use the model on US infrastructure. Because AI Gateway serves the models from multiple providers, it automatically routes across them for failover, higher uptime, and more available throughput than any single provider offers. You call the same moonshotai/kimi-k3 model ID, and the gateway handles provider selection and fallback. Kimi K3 Fast trades a higher per-token cost for lower latency. Request it with the speed option on the base model, which stays on moonshotai/kimi-k3 and falls back to standard speed when the fast tier is unavailable. Alternatively, use moonshotai/kimi-k3-fast. The fast variant costs ~50% more than the base model. To use Kimi K3, set model to moonshotai/kimi-k3 in the AI SDK: US inference To route Kimi K3 requests to use only US data centers for inference, set inferenceRegion. Regional pricing is ~10% more than the regular variant. Zero Data Retention Zero Data Retention for Kimi K3 is also available. Turn on Zero Data Retention for every request from the AI Gateway dashboard settings, or set it per request with zeroDataRetention: Providers and endpoints To see every provider serving Kimi K3, along with per-provider pricing, supported parameters, uptime, throughput, and latency, call the model endpoints API: Model prices vary by provider and variant type. Use Kimi K3 in your coding agent Run vercel ai-gateway coding-agents setup and select Kimi K3. This will detect the agents on your machine, provision an AI Gateway key, and write their config. See how to set it up via the Vercel CLI. Try Kimi K3 in the model playground. Read more ## Worth Reading 071926 DevFeed: [Worth Reading 071926]() Original publisher: [Read original article]() Author: Russ Published: 2026-07-19T12:25:09Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [Ethernet](), [Language models](), [data centers](), [Server](), [WebRTC](), [Streaming](), [Web](), [idc](), [Google Meet]() Tags: [data-centers](), [ethernet](), [idc](), [model](), [server](), [video](), [web](), [webrtc](), [worth-reading]() ### AI overview A developer-oriented roundup covering Ethernet switch and server-market statistics, the need to treat language-model output as untrusted, video delivery technologies including WebRTC and DASH, AI-mediated content consumption, and the potential costs and environmental effects of data centers in low Earth orbit. ### Source excerpt In fact, in Q1 2026, the Ethernet switch market grew faster than the overall server market did according to the latest statistics from IDC. We are tempted to treat a capable language model as a knowledgeable colleague whose conclusions we can accept. It is more accurate, and more useful, to treat it as an untrusted component that produces plausible output which must be checked before it is relied upon. In my view, MoQ occupies a middle ground between WebRTC (which is used for lots of video conferencing applications like Google Meet) and DASH (Dynamic Adaptive Streaming over HTTP) which powers most entertainment video streaming on the web. Content consumption is detached from the website itself, as users rely on AI-driven systems to aggregate, summarize and contextualize information without visiting the original source. Instead of researching across multiple tabs, readers ask an AI system to do the work for them. Shifting data centers from earth to space has become an alternative touted as solving concerns without generating new ones. In sun synchronous, low Earth orbit, orbiting data centers ("ODCs") may have comparatively less environmental impact and lower operating costs. ## NVIDIA GPU Ecosystem, Simply Explained DevFeed: [NVIDIA GPU Ecosystem, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-06-30T11:37:56Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [GPU](), [Nvidia](), [Tensor Cores](), [data centers](), [Large Language Model]() Tags: [architecture](), [data-centers](), [gpu](), [guide](), [llms](), [nvidia](), [parallel](), [tensor-cores]() ### AI overview A plain-English guide to NVIDIA GPU architecture, including parallel processing, Streaming Multiprocessors, CUDA and Tensor Cores, GPU memory, interconnects, and scaling for AI workloads in data centers. ### Source excerpt A guide to NVIDIA GPU architecture, interconnects, and scaling in plain English. ## Data Center Fabrics High Level Overview DevFeed: [Data Center Fabrics High Level Overview]() Original publisher: [Read original article]() Author: j2sw Published: 2026-06-17T12:30:00Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [data centers](), [Network architectures](), [networking](), [Latency]() Tags: [architecture](), [data-center](), [ecmp](), [internet-architecture](), [latency](), [leaf-switch](), [network](), [servers](), [spine-switch](), [storage](), [vxlan]() ### AI overview This article explains data center fabrics as networks that connect servers, storage systems, and other network devices. It describes the common leaf-and-spine architecture, the roles of leaf and spine switches, packet movement, and how multiple paths provide bandwidth and predictable latency. ### Source excerpt A data center fabric connects servers, storage, and network devices using a leaf-spine architecture designed for high bandwidth and predictable latency. Learn how packets move through a fabric, why ECMP matters, and how modern data centers support virtualization, cloud, and AI workloads. The post Data Center Fabrics High Level Overview appeared first on Justin Wilson (j2sw). ## Startup's nuclear-inspired cooling system could make data centers more sustainable DevFeed: [Startup's nuclear-inspired cooling system could make data centers more sustainable]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-10T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [data centers](), [Artificial Intelligence](), [cloud-infrastructure](), [Computer science]() Tags: [ai](), [ai-data-centers](), [artificial-intelligence](), [computer-science-and-technology](), [cooling](), [data-center-cooling](), [data-center-sustainability](), [data-centers](), [efficiency](), [energy](), [energy-efficiency](), [energy-storage](), [ferveret](), [matteo-bucci](), [nuclear-science-and-engineering](), [power](), [reza-azizian](), [school-of-engineering](), [startups](), [sustainability](), [sustainable-computing](), [technology-and-society](), [water]() ### AI overview Ferveret, a startup founded by two MIT researchers, is adapting nuclear-reactor heat-transfer methods to cool AI data-center servers with a specialized liquid. Its Adaptive Phase Cooling system uses small, frequently detaching bubbles to improve heat transfer without water and with less electricity. ### Source excerpt Founded by two researchers from MIT, Ferveret reduces the amount of energy and water required to cool the chips that power AI. ## An Opinionated Critique of AI's Environmental, Social, and Infrastructure Impacts DevFeed: [An Opinionated Critique of AI's Environmental, Social, and Infrastructure Impacts]() Original publisher: [Read original article]() Published: 2026-05-05T15:09:12Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [Artificial Intelligence](), [Code](), [large-language-models](), [data centers](), [data]() Tags: [ai](), [ai-data-centers](), [climate](), [co2](), [code](), [data-centers](), [education](), [energy](), [hiring](), [language-models](), [large-language-models](), [water]() ### AI overview This opinion article argues that society is being asked to accept damaging conditions as normal, focusing on climate change, AI data centers' resource use, geopolitical crises, and dependence on US cloud infrastructure. It is especially critical of AI's effects on hiring and education, and of deploying unproven large language model output in production services. ### Source excerpt "Alice laughed. 'There's no use trying,' she said. 'One can't believe impossible things.' I daresay you haven't had much practice,' said the Queen. 'When I was your age, I always did it for half-an-hour a day. Why, sometimes I've believed as many as six impossible things before breakfast." - Through the looking-glass, Lewis Carrol by John Tenniel To stay sane, we have to accept that our climate is going completely haywire, but that it is ok to mostly ignore that since saving ourselves is apparently not cost-effective. ## Guide: How to Unify Identity Across Cloud and Data Center Infrastructure DevFeed: [Guide: How to Unify Identity Across Cloud and Data Center Infrastructure]() Original publisher: [Read original article]() Author: info@goteleport.com (Mayur Pipaliya) Published: 2026-05-01T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [IAM](), [trust](), [certificates](), [SPIFFE](), [Cloud](), [data centers](), [Amazon Web Services](), [Entra ID]() Tags: [certificates](), [cloud](), [critical-infrastructure](), [data-center](), [entra-id](), [identity](), [identity-and-access](), [infrastructure](), [spiffe]() ### AI overview This guide explains identity fragmentation across cloud accounts, data centers, and colocated infrastructure. It describes siloed identity systems, credential sprawl, and differing access models, and presents approaches including hardware roots of trust, short-lived certificates, shared certificate authorities, SPIFFE workload identities, reverse tunnels, and protocol-level enforcement. ### Source excerpt Inside this guide, discover the root causes of identity fragmentation across cloud and data center environments -- and what it takes to unify identity. ## Building the compute infrastructure for the Intelligence Age DevFeed: [Building the compute infrastructure for the Intelligence Age]() Original publisher: [Read original article]() Published: 2026-04-29T15:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [stargate](), [AI Infrastructure](), [OpenAI](), [data centers](), [cloud-infrastructure](), [Cloud]() Tags: [ai](), [ai-infrastructure](), [cloud-infrastructure](), [compute](), [data-center](), [energy](), [global-affairs](), [infrastructure](), [intelligence-age](), [openai](), [operations](), [partners](), [stargate]() ### AI overview OpenAI describes Stargate as a long-term effort to expand the compute infrastructure needed to deliver AGI broadly and reliably. The article highlights rapidly growing AI demand, progress toward U.S. capacity targets, and partnerships spanning cloud infrastructure, data centers, chips, energy, construction, finance, and operations. ### Source excerpt OpenAI scales Stargate to build the compute infrastructure powering AGI, adding new data center capacity to meet growing AI demand. ## Hedge 303: Data Centers? DevFeed: [Hedge 303: Data Centers?]() Original publisher: [Read original article]() Author: Russ Published: 2026-04-23T18:43:53Z Content type: opinion Language: en Sources: [rule 11 reader]() Topics: [data centers]() Tags: [audio](), [data-centers](), [download](), [fcc](), [hedge](), [policy](), [us]() ### AI overview A roundtable discussion examines why data centers are being built and considers the FCC's ban on non-US-made home routers, including whether the policy was appropriate and what alternatives might exist. ### Source excerpt In this roundtable episode of the Hedge, Eyvonne, Tom, and Russ hang out and talk about data centers--why are we building all these things again? Our second topic is the FCC's ban on non-US made home routers. Was this the right thing to do? Was it the wrong thing to do? Were there any other policy options? [Next page]()