# data center Published articles for data center. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Native Splunk brings real-time insights to Cisco Nexus One DevFeed: [Native Splunk brings real-time insights to Cisco Nexus One]() Original publisher: [Read original article]() Author: David Keith Published: 2026-09-16T15:00:59Z Content type: release Language: en Sources: [Cisco Blogs]() Topics: [Nexus Dashboard](), [observability](), [telemetry](), [datacenter](), [Security](), [sensitive data](), [audit](), [configuration]() Tags: [analytics](), [audit](), [cisco](), [cisco-nexus-dashboard](), [configuration](), [data-center](), [data-center-networking](), [network](), [nexus-one](), [observability](), [real-time](), [sensitive-data](), [splunk](), [troubleshooting]() ### AI overview Cisco describes native Splunk embedded in Cisco Nexus Dashboard as an on-premises analytics and observability capability for data center and AI workloads. It processes telemetry locally, correlates network, security, configuration, and audit data, and provides dashboards, searches, and alerts for troubleshooting, data sovereignty, compliance, and cost efficiency. ### Source excerpt Discover how native Splunk embedded in Cisco Nexus Dashboard delivers real-time analytics, faster troubleshooting, and on-premises data sovereignty for modern data center and AI workloads. ## 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 ## Cisco UCS Manager: Celebrating the legacy, shaping the future with Cisco Intersight DevFeed: [Cisco UCS Manager: Celebrating the legacy, shaping the future with Cisco Intersight]() Original publisher: [Read original article]() Author: Jacob Van Ewyk Published: 2026-09-15T21:00:37Z Content type: opinion Language: en Sources: [Cisco Blogs]() Topics: [Cisco](), [Server](), [Automation](), [configuration](), [API](), [legacy]() Tags: [api](), [automation](), [cisco](), [data-center](), [end-of-life](), [eol](), [infrastructure](), [legacy](), [lifecycle](), [management](), [server]() ### AI overview Cisco describes the legacy of Cisco UCS Manager and positions Cisco Intersight as the strategic platform for future Cisco UCS software management. The article also outlines announced end-of-sale and end-of-life milestones, including a planned Last Date of Support of December 31, 2030, subject to applicable contracts and service terms. ### Source excerpt Cisco UCS Manager shaped 17 years of infrastructure management. Now, with its end-of-life announced, Cisco Intersight carries that foundation forward with centralized visibility, automation, and scale. Learn the key dates for the UCS Manager EOL. ## Astera Labs Releases Leo 2 CXL Memory Controllers and Leo X Controller for Rackscale Fabric-Attached Memory DevFeed: [Astera Labs Releases Leo 2 CXL Memory Controllers and Leo X Controller for Rackscale Fabric-Attached Memory]() Original publisher: [Read original article]() Author: Ryan Smith Published: 2026-09-15T17:00:48Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [pcie](), [systems](), [datacenter]() Tags: [astera-labs](), [cxl](), [data-center](), [ddr4](), [ddr5](), [leo](), [memory](), [other-components](), [pcie](), [releases](), [server]() ### AI overview Astera Labs is launching Leo 2 CXL smart memory controllers with DDR4 support and CXL 3.2/PCIe Gen6 connectivity, alongside the Leo-X controller for fabric-attached memory in rack-scale systems. ### Source excerpt Astera Labs is launching a new generation of Leo smart memory controllers. The Leo 2 series adds support for CXL 3.2 and PCIe Gen6, while the ambitious Leo X brings the ability to attach memory expanders directly to the fabric networks of AI accelerators The post Astera Labs Releases Leo 2 CXL Memory Controllers and Leo X Controller for Rackscale Fabric-Attached Memory appeared first on ServeTheHome. ## Closing the Resilience Gap with Native Splunk in Cisco Nexus One DevFeed: [Closing the Resilience Gap with Native Splunk in Cisco Nexus One]() Original publisher: [Read original article]() Author: Murali Gandluru Published: 2026-09-14T19:55:29Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Resilience](), [telemetry](), [Network](), [dashboards](), [Architecture & Design](), [data](), [Artificial Intelligence]() Tags: [2026](), [ai](), [architecture](), [business](), [cisco](), [cisco-data-fabric](), [dashboards](), [data](), [data-center](), [infrastructure](), [network](), [nexus-dashboard](), [nexus-one](), [operational](), [resilience](), [telemetry]() ### AI overview Cisco describes expanding Native Splunk in Cisco Nexus One from a single-node to a multi-node architecture. The integration brings Splunk search, dashboards, alerting, and analytics together with authoritative network context and distributed application, infrastructure, and security data through Cisco Data Fabric, helping teams investigate incidents and move from telemetry to root cause faster. ### Source excerpt See how Native Splunk and Cisco Nexus One bring analytics closer to network data, strengthen resilience, and connect teams across operational domains. ## FS Pairs 1.6T Scale-Out Optics With 500 km Coherent Modules and a Handheld Toolkit for AI Fabrics DevFeed: [FS Pairs 1.6T Scale-Out Optics With 500 km Coherent Modules and a Handheld Toolkit for AI Fabrics]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T17:34:25Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [GPU](), [InfiniBand](), [Networks](), [Ethernet](), [datacenter](), [Nvidia](), [data]() Tags: [announcement](), [data-center](), [enterprise](), [ethernet](), [gpu](), [infiniband](), [networking](), [networks](), [nvidia]() ### AI overview FS presents a two-part optics portfolio for AI networking: 400G, 800G, and 1.6T Scale-Out transceivers for links within GPU clusters, plus 400G and 800G Scale-Across coherent modules for connecting clusters across sites up to 500 km. The announcement also introduces the BOX 5 Ultra handheld toolkit for configuring, validating, and monitoring transceivers from 100M to 1.6T. ### Source excerpt FS has organized its AI optics into a two-part portfolio: Scale-Out transceivers at 400G, 800G, and 1.6T for the links inside a GPU cluster, and Scale-Across coherent modules at 400G and 800G for stitching clusters together across sites at distances up to 500 km. The Scale-Out side covers Ethernet, RoCE, and InfiniBand fabrics between GPU The post FS Pairs 1.6T Scale-Out Optics With 500 km Coherent Modules and a Handheld Toolkit for AI Fabrics appeared first on StorageReview.com. ## d-Matrix Joins the NVIDIA NVLink Fusion Platform DevFeed: [d-Matrix Joins the NVIDIA NVLink Fusion Platform]() Original publisher: [Read original article]() Author: Cliff Robinson Published: 2026-09-12T21:42:59Z Content type: news Language: en Sources: [ServeTheHome]() Topics: [d-matrix](), [NVLink](), [Nvidia](), [xpu](), [AI Inference](), [datacenter](), [networking](), [cpu](), [Spectrum-X]() Tags: [accelerators](), [ai](), [ai-accelerator](), [ai-inference](), [d-matrix](), [data-center](), [ethernet](), [networking](), [nvidia](), [nvidia-vera](), [nvlink](), [server](), [xpu]() ### AI overview d-Matrix and NVIDIA announced that d-Matrix will bring its next-generation XPUs to the NVLink Fusion platform. The integration is intended to support scaling from individual Raptor XPUs to larger rack-scale and clustered deployments for AI inference, alongside NVIDIA networking and CPU technologies. ### Source excerpt d-Matrix and NVIDIA announced that d-Matrix will use NVLink Fusion to scale up and out with its next-gen Raptor AI accelerators The post d-Matrix Joins the NVIDIA NVLink Fusion Platform appeared first on ServeTheHome. ## A Humorous List of Physical Alternatives to GitHub for Storing Projects DevFeed: [A Humorous List of Physical Alternatives to GitHub for Storing Projects]() Original publisher: [Read original article]() Author: Sulaiman Bah Published: 2026-09-12T12:00:11Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [GitHub](), [datacenter](), [RAID](), [USB]() Tags: [data-center](), [github](), [project-team-management](), [raid](), [storage](), [the-software-life](), [usb](), [version-control]() ### AI overview This humorous commentary proposes physical storage options as alternatives to GitHub, including a homemade 50-petabyte data center, a 64-gigabyte USB stick, and a 2-terabyte external hard drive. ### Source excerpt Every developer knows just how important it is to share work with a coworker. If you are working on a critical feature in a crunch, prompt peer reviews and instant feedback can mean the difference between a secure contract and an angry client. So it follows that when the infrastructure we rely on to work [...] The post GitHub Alternative List: Quintessential Options appeared first on Atomic Spin. ## 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. ## From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry DevFeed: [From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [datacenter](), [networking](), [Software](), [data]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [blackwell](), [compute](), [cuopt](), [data-center](), [gb200](), [infrastructure](), [llms](), [manufacturing](), [memory](), [nemo](), [nemotron](), [networking](), [nvidia](), [nvidia-blackwell](), [nvl72](), [performance](), [production](), [real-time](), [software](), [supply-chain](), [vera-rubin]() ### AI overview NVIDIA describes how it measures and reduces the time from wafer-out to first token across complex Grace Blackwell and Vera Rubin supply chains. The article focuses on time-to-rack, critical material allocation, real-time visibility, redundancy, reliability, and codifying human expertise. ### Source excerpt NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two... ## Now generally available: See how Stack Automation by Quali cuts deployment time DevFeed: [Now generally available: See how Stack Automation by Quali cuts deployment time]() Original publisher: [Read original article]() Author: Carlos Campos Torres Published: 2026-09-09T15:00:14Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Automation](), [Deployment](), [Provisioning](), [configuration](), [cloud-infrastructure](), [Cloud]() Tags: [agenticops](), [automation](), [cloud](), [data-center](), [deployment](), [documentation](), [infrastructure](), [infrastructure-as-code-iac](), [platform](), [software](), [ubuntu](), [windows]() ### AI overview Stack Automation by Quali, co-developed with Cisco, is generally available as a deployment automation platform for validated Cisco and third-party infrastructure solutions. Its Solutions Hub supports planning, modeling, bill of materials generation, and automated deployment across on-premises and public-cloud services, reducing deployment cycles from weeks to hours or minutes. ### Source excerpt Still burning weeks on manual infrastructure deployment? Stack Automation by Quali, co-developed with Cisco, is now available--turning that grind into an automated, cloud-like experience that takes minutes. ## Join our live webinars: Migrating from Atlassian to YouTrack DevFeed: [Join our live webinars: Migrating from Atlassian to YouTrack]() Original publisher: [Read original article]() Author: Elena Pishkova Published: 2026-09-09T12:51:07Z Content type: news Language: en Sources: [The JetBrains Blog]() Topics: [migration](), [AI Bots]() Tags: [ai-agents](), [atlassian](), [best-practices](), [data-center](), [demo](), [deployment](), [documentation](), [events](), [helpdesk](), [knowledge-base](), [livestreams](), [mcp](), [migration](), [project-management](), [tutorials](), [webinar](), [workflows](), [youtrack](), [youtrack-server]() ### AI overview JetBrains announces live webinars about migrating from Atlassian products to YouTrack. Sessions cover moving projects, users, and data from Jira, Confluence, and Jira Service Management, with migration demos, deployment and pricing information, customer stories, and regional sessions. ### Source excerpt Atlassian is discontinuing sales and support for Data Center products. If you're exploring alternatives, join us for a live session on September 30 to see how Jira-to-YouTrack migration works, including a demo and real customer stories. Register for the worldwide English-language webinar, hosted by the YouTrack team, or attend a regional session in Japanese hosted [...] ## AI-Ready Private Cloud with Cisco and VMware DevFeed: [AI-Ready Private Cloud with Cisco and VMware]() Original publisher: [Read original article]() Author: sabina anja Published: 2026-09-08T15:33:39Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Artificial Intelligence](), [Network](), [Cloud](), [networking](), [cloud-infrastructure](), [distributed-training](), [datacenter](), [GPU](), [Latency](), [Inference](), [model-deployment](), [inference-endpoints]() Tags: [ai](), [cisco](), [cloud](), [cloud-infrastructure](), [cloud-platform](), [data-center](), [distributed-training](), [fabric](), [gpu](), [home-page](), [inference](), [inference-endpoints](), [latency](), [networking](), [object-storage](), [pipelines](), [private-cloud](), [telemetry](), [vcf-9-1](), [vcf-networking](), [vmware](), [vmware-cloud-foundation]() ### AI overview This article explains why an AI-ready private cloud requires more than adding GPUs. It focuses on how Broadcom and Cisco are integrating VMware Cloud Foundation with Cisco Nexus One Fabric to address AI workload networking, including bandwidth-intensive east-west traffic, bursty north-south traffic, latency, congestion management, and telemetry across virtual and physical infrastructure. ### Source excerpt An AI-ready private cloud is not simply a private cloud with GPUs added to it. What determines whether a private cloud platform can actually serve AI workloads effectively is everything built around them: how the fabric carries traffic, how the tenancy model lets teams consume capacity, and how policy and telemetry stay coherent across the ... Continued The post AI-Ready Private Cloud with Cisco and VMware appeared first on VMware Blogs. ## Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free DevFeed: [Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-03T17:00:39Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Benchmark](), [benchmarking](), [datacenter](), [GB200](), [GPU](), [Server](), [dell]() Tags: [benchmark](), [compute](), [data-center](), [dell](), [efficiency](), [enterprise](), [gb200](), [gpu](), [hdd](), [measurements](), [nvl72](), [performance](), [servers](), [ssd](), [storage]() ### AI overview This article evaluates replacing eight 30TB nearline HDDs with one 245TB Micron 6600 ION SSD. Its measurements indicate lower power consumption, substantially higher read efficiency, and a reduction from 22 racks of HDD storage to six racks of flash at exabyte scale, potentially freeing enough power for a GB200 NVL72. ### Source excerpt For two decades, the SSD-versus-HDD conversation ended the same way: flash wins on performance, disk wins on price per terabyte, and the size of that price gap settled the argument in favor of bulk storage. As storage technology has matured and AI has taken over, that framing is clearly out of date. The largest data The post Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free 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. ## How we could save petabytes of cache storage with Zstandard and Pingora DevFeed: [How we could save petabytes of cache storage with Zstandard and Pingora]() Original publisher: [Read original article]() Author: Aashi Patel Published: 2026-09-01T12:59:00Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [Caching](), [Pingora](), [cloud-infrastructure](), [Hardware]() Tags: [cache](), [cloudflare](), [compression](), [cpu](), [data-center](), [hardware](), [internship-experience](), [performance](), [pingora](), [prototyping](), [storage](), [testing]() ### AI overview Cloudflare describes a Cache Transcoding prototype that stores eligible cached assets in Zstandard-compressed form. The approach aims to increase effective cache capacity and reduce inter-data-center transfers while adding a small CPU cost during cache fills. ### Source excerpt Could we get more cache space with the same hardware? We prototyped compression inside Cloudflare's cache to find out. ## How we saved 100 terabytes of memory by optimizing 1.1.1.1's DNS cache DevFeed: [How we saved 100 terabytes of memory by optimizing 1.1.1.1's DNS cache]() Original publisher: [Read original article]() Author: Sebastiaan Neuteboom Published: 2026-08-27T17:02:35Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [Cache](), [Caching](), [Cloudflare](), [Rust](), [benchmarking](), [Latency](), [datacenter]() Tags: [1-1-1-1](), [benchmarking](), [cache](), [cloudflare](), [data-center](), [deep-dive](), [dns](), [engineering](), [latency](), [memory](), [net-maui](), [optimization](), [performance](), [rust]() ### AI overview Cloudflare describes five Rust-level changes to the memory layout of Big Pineapple, the platform behind 1.1.1.1 and other DNS services. The changes reduced DNS cache entry size by over 50%, freed roughly 100 terabytes of memory across the fleet, increased insert throughput by 43%, and reduced lookup latency by 19%. ### Source excerpt Five Rust-level memory optimizations to the DNS cache layout of Big Pineapple cut per-entry memory by 56%, freeing approximately 100 TB of memory across Cloudflare's fleet. ## What N, N+1, 2N, and 2(N+1) Mean in a Data Center DevFeed: [What N, N+1, 2N, and 2(N+1) Mean in a Data Center]() Original publisher: [Read original article]() Author: j2sw Published: 2026-08-27T13:59:08Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [datacenter](), [data]() Tags: [2n-redundancy](), [capacity](), [colocation](), [components](), [cooling](), [data-center](), [data-center-power](), [equipment](), [internet-architecture](), [maintenance](), [n-plus-1-redundancy](), [network-operations](), [power-distribution](), [system](), [ups]() ### AI overview This article explains what data center redundancy ratings N, N+1, N+2, 2N, and 2(N+1) indicate about component capacity, failures, maintenance, cooling, and alternate paths. ### Source excerpt Have you ever looked at a data center marketing slick and wondered what N, N+1, 2N, and 2(N+1) actually mean? The main thing they tell you is how many components can fail before the system drops below the capacity needed to support the load. When I evaluate a data center, I want to know what ... Read more The post What N, N+1, 2N, and 2(N+1) Mean in a Data Center appeared first on Justin Wilson (j2sw). ## NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure DevFeed: [NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure]() Original publisher: [Read original article]() Author: Farshad Ghodsian Published: 2026-08-26T21:06:58Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [AI Infrastructure](), [Nvidia](), [AI Platform](), [datacenter](), [Inference](), [systems]() Tags: [accelerate](), [agentic-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [architecture](), [compute](), [data-center](), [data-center-cloud](), [deployment](), [inference](), [infrastructure](), [integration](), [memory](), [model](), [models](), [networking-communications](), [nvidia](), [nvlink](), [performance](), [platform](), [scale](), [support](), [top-stories]() ### AI overview NVIDIA NVLink Fusion connects custom XPUs and CPUs to NVIDIA's AI infrastructure platform, while NVHBM provides validated HBM base-die technology intended to increase memory bandwidth, save package area, and reduce power consumption. The article describes benefits for training and large-scale inference, including up to 30% more memory bandwidth per stack than standard HBM4e. ### Source excerpt AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads,... ## How Speculative Decoding Can Make LLM Generation 2-3 Times Faster DevFeed: [How Speculative Decoding Can Make LLM Generation 2-3 Times Faster]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-26T15:30:34Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [text-generation](), [GPU]() Tags: [article](), [data-center](), [decoding](), [generation](), [gpu](), [how-to](), [llms](), [tokens]() ### AI overview This tutorial explains speculative decoding, in which a smaller model proposes candidate tokens and a larger model evaluates them in a single forward pass. It covers autoregressive generation, GPU utilization, candidate acceptance and rejection, output-quality preservation, acceptance rates, draft sources, and when the technique may stop helping. ### Source excerpt In this article, we will look at how speculative decoding works. ## Mistral x HUMAIN DevFeed: [Mistral x HUMAIN]() Original publisher: [Read original article]() Published: 2026-08-24T16:02:41Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [AI Strategy](), [AI Infrastructure](), [AI Models](), [Model Development](), [model-deployment](), [Localization (l10n)](), [datacenter](), [Cybersecurity]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [arabic](), [data](), [data-center](), [deployment](), [enterprise](), [europe](), [financial-services](), [manufacturing](), [model-development](), [public-sector](), [security]() ### AI overview Mistral and HUMAIN announce a strategic collaboration to advance sovereign AI in Saudi Arabia and across the Middle East. The initiative covers AI infrastructure, advanced model development, localized Arabic-capable models, and deployment of AI solutions for regulated industries. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules DevFeed: [Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-24T15:08:39Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Spectrum-X](), [Ethernet](), [Generative AI](), [networking](), [datacenter](), [Nvidia]() Tags: [ai](), [ai-agent](), [ai-factory](), [ai-networking](), [data-center](), [data-center-cloud](), [ethernet](), [generative-ai](), [infrastructure](), [internet-communications](), [latency](), [low-latency](), [networking-communications](), [nvidia](), [resilience](), [spectrum-x]() ### AI overview This article explains how the growth of distributed generative AI training has made scale-out networking a major data center performance bottleneck. It contrasts traditional Ethernet with NVIDIA Spectrum-X Ethernet, a hardware-accelerated architecture that co-designs switches and host-side NICs to provide predictable low latency, high fabric utilization, and resilience for large AI workloads. It also introduces Spectrum-X Multiplane technology and describes how AI collective communication exposes limitations in conventional ECMP routing and congestion handling. ### Source excerpt The massive growth of generative AI has fundamentally altered data center design. As distributed model training scales to span hundreds of thousands of GPUs,... ## Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS DevFeed: [Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Factory](), [Artificial Intelligence](), [Optimization](), [datacenter](), [GPU](), [Inference](), [Nvidia](), [Provisioning](), [systems]() Tags: [ai](), [ai-factory](), [ai-inference](), [compute](), [data-center](), [data-center-cloud](), [dsx](), [efficiency](), [gpu](), [inference](), [infrastructure](), [nvidia](), [nvidia-dsx](), [optimization](), [performance](), [scale](), [tokens](), [vera-rubin-nvl72]() ### AI overview NVIDIA DSX MaxLPS is presented as a suite of chip, thermal, system, and software technologies for increasing AI factory throughput within a fixed power budget. The article emphasizes application-level performance per watt, dynamic power allocation, software power optimization, and warm-water liquid cooling as ways to convert more site power into AI inference output. ### Source excerpt AI factories are power-constrained industrial systems. The question is no longer how many GPUs fit in a data center, but how much AI output each available... ## 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. [Next page]()