# data centers Published articles for data centers. 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 [...] ## 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. ## Cloudflare Tests Cache Transcoding to Reduce Storage Requirements DevFeed: [Cloudflare Tests Cache Transcoding to Reduce Storage Requirements]() Original publisher: [Read original article]() Author: Renato Losio Published: 2026-09-13T10:35:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Caching](), [Transcodings](), [cloud-infrastructure](), [Pingora]() Tags: [architecture-design](), [cache](), [caching](), [cdn](), [cloud](), [cloudflare](), [cloudflare-cache-transcoding](), [compression](), [cpu](), [data-centers](), [development](), [edge-computing](), [news](), [pingora](), [rust](), [storage]() ### AI overview Cloudflare is testing Cache Transcoding, a prototype that Zstandard-compresses eligible uncompressed text before it is stored in cache. The approach aims to increase effective cache capacity and reduce inter-data-center transfer, with configurable CPU and storage trade-offs. ### Source excerpt Cloudflare recently described a prototype called Cache Transcoding that compresses eligible cache content, mainly uncompressed text such as HTML, JSON, CSS, and JavaScript, using Zstandard before storing it on disk. The hyperscaler estimates that the approach could provide petabytes of additional effective cache capacity, although broader testing is still needed. By Renato Losio ## 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. ## On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch DevFeed: [On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch]() Original publisher: [Read original article]() Author: Tom Fenton Published: 2026-09-01T18:28:19Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Generative AI](), [on-prem](), [cloud-infrastructure](), [datacenter](), [Cloud](), [data]() Tags: [2026](), [ai](), [cloud](), [data-centers](), [enterprise](), [enterprise-storage](), [generative-ai](), [infrastructure](), [on-prem](), [technology]() ### AI overview At VMware Explore 2026, StorageReview discusses Lenovo's partnership with VMware around turnkey generative AI infrastructure, the return to on-premises deployments, and memory supply constraints. The article describes moving memory tiering into the VMware Cloud Foundation hypervisor layer, using NVMe drives to reduce dependence on expensive DRAM without requiring application or operating-system rewrites. ### Source excerpt At VMware Explore 2026, we had the chance to sit down with Stuart McRae, Executive Director of Lenovo's Enterprise Storage, Software, and Solutions Offering Group. Rather than focusing on a specific product or technology, as we often do, this conversation took a broader view. We covered everything from how AI is reshaping buyer options and The post On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch appeared first on StorageReview.com. ## Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS DevFeed: [Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS]() Original publisher: [Read original article]() Author: Sandeep Singh Published: 2026-09-01T14:01:10Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Orchestration](), [Kubernetes](), [Amazon EKS](), [AWS Lambda](), [Server](), [Hardware]() Tags: [amazon-dynamodb](), [amazon-eks](), [aws](), [aws-lambda](), [aws-step-functions](), [data-centers](), [deployment](), [event-driven](), [hardware](), [kubernetes](), [observability](), [orchestration](), [server](), [serverless](), [thought-leadership]() ### AI overview Tutorial on designing an AWS-based hybrid cloud orchestration system for centralized lifecycle management of distributed on-premises servers and EKS Anywhere clusters. ### Source excerpt Learn how to build a hybrid cloud orchestration solution that manages distributed on-premises infrastructure at scale using AWS serverless technologies and Amazon EKS Anywhere. Part 1 covers the core event-driven architecture patterns for automating server lifecycle and cluster management across hundreds of sites. ## 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. ## Xbox CEO Asha Sharma once again stresses Xbox's need for affordability DevFeed: [Xbox CEO Asha Sharma once again stresses Xbox's need for affordability]() Original publisher: [Read original article]() Author: Nicole Carpenter Published: 2026-08-27T14:40:00Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [Xbox](), [Microsoft](), [Hardware](), [data centers]() Tags: [bbc](), [ceo](), [company](), [data-centers](), [hardware](), [microsoft](), [xbox]() ### AI overview Xbox CEO Asha Sharma says the company needs to improve affordability and efficiency as it addresses declining revenue, rising component costs, layoffs, and uncertainty about its next-generation console. ### Source excerpt Xbox isn't healthy, she said again. ## AI helps design new materials that work in the real world DevFeed: [AI helps design new materials that work in the real world]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-26T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Framework](), [Simulation and Design](), [Crystal]() Tags: [ai](), [artificial-intelligence](), [bowen-yu](), [chemical-engineering](), [chemistry](), [computer-chips](), [computer-science-and-technology](), [crystal](), [crysvcd](), [data-centers](), [department-of-energy-doe](), [diffusion-models](), [dmse](), [hao-tang](), [heather-kulik](), [ju-li](), [machine-learning](), [materials-design](), [materials-discovery](), [materials-science-and-engineering](), [mingda-li](), [mouyang-cheng](), [national-science-foundation-nsf](), [nuclear-science-and-engineering](), [paper](), [physics](), [research](), [school-of-engineering](), [school-of-science](), [semiconductors](), [weiliang-luo](), [weiwei-xie](), [yongqiang-cheng]() ### AI overview MIT researchers developed CrysVCD, a framework that applies chemistry-based valence constraints before material generation to improve the stability of generated designs. In tests, it achieved high lattice-dynamics stability in nearly 70 percent of computational material generations and supported targeting properties such as high thermal conductivity and high dielectric constant. ### Source excerpt The "CrysVCD" tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs. ## Push-button migration from Confluent to Redpanda with Shadowing DevFeed: [Push-button migration from Confluent to Redpanda with Shadowing]() Original publisher: [Read original article]() Author: Trevor Blackford Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [migration](), [Replication](), [Kafka](), [Confluent Cloud](), [Confluent Platform](), [Disaster Recovery](), [Usability]() Tags: [big-bang](), [confluent-cloud](), [confluent-platform](), [data-centers](), [disaster-recovery](), [kafka](), [migration](), [replication](), [schema]() ### AI overview The article presents Redpanda Shadowing 26.2 as a low-risk migration path from Confluent Cloud, Confluent Platform, or other Apache Kafka-compatible clusters. It replicates topic data, schemas, offsets, and ACLs while preserving offsets, allowing teams to validate workloads and move applications individually instead of using a big-bang cutover. ### Source excerpt Migrate off Confluent without the "big cutover weekend." Redpanda Shadowing carries topic data, schemas, offsets, and ACLs on a single link. Available on Self-Managed , BYOC, and Dedicated. ## 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. ## NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories DevFeed: [NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Factory](), [AI Infrastructure](), [Artificial Intelligence](), [networking](), [Nvidia](), [cloud-infrastructure](), [datacenter](), [Cloud](), [NVLink](), [InfiniBand]() Tags: [agentic-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [bluefield-dpu](), [connectx](), [data-center-cloud](), [data-centers](), [dsx](), [grace-cpu](), [infiniband](), [infrastructure](), [network](), [networking](), [networking-communications](), [nvidia](), [nvlink](), [top-stories](), [vera-cpu](), [vera-rubin]() ### AI overview NVIDIA describes Scale-In network infrastructure for agentic AI factories, centered on BlueField-4, DOCA, and Spectrum-X Ethernet. The architecture is intended to accelerate networking, storage, security, data movement, tenant isolation, and infrastructure operations as AI compute scales. ### Source excerpt Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,... ## Why GitHub feels less reliable lately DevFeed: [Why GitHub feels less reliable lately]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-23T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [GitHub](), [incident](), [migration](), [systems](), [Azure](), [GitHub Actions](), [pull-requests]() Tags: [autoscaling](), [capacity](), [concurrency](), [data-centers](), [deployment](), [github](), [incident](), [incident-management](), [istio](), [reliability](), [request](), [resilience](), [sre](), [transformation]() ### AI overview The article argues that GitHub's recent reliability problems reflect the difficult middle of a major infrastructure transformation. It connects incidents to migration complexity, unsafe automation, configuration mistakes, capacity and concurrency weaknesses, database migration errors, and autoscaling problems. ### Source excerpt GitHub is not having one outage problem. Its recent incident reports show the difficult middle of a platform transformation. ## 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 ## Building AI infrastructure with the Effingham County community DevFeed: [Building AI infrastructure with the Effingham County community]() Original publisher: [Read original article]() Published: 2026-07-22T13:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Infrastructure](), [datacenter](), [OpenAI](), [codex]() Tags: [ai-infrastructure](), [codex](), [community](), [data-center](), [data-centers](), [global-affairs](), [infrastructure](), [jobs](), [openai]() ### AI overview OpenAI outlines Project Camellia, a planned datacenter in Effingham County, Georgia, including phased power delivery, a closed-loop water system, community benefits, local jobs, tax revenue, and Codex credits. ### Source excerpt OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex. ## 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. [Next page]()