# AI Infrastructure AI infrastructure comprises the compute, data, orchestration, and control-plane layers used to run training and inference workloads, along with tooling to provision, govern, and operate them. 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 [...] ## Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck DevFeed: [Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-15T17:23:54Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Data Infrastructure](), [AI Infrastructure](), [idc](), [Inference](), [Retrieval-Augmented Generation](), [synthetic-data]() Tags: [ai](), [compute](), [data-infrastructure](), [datasets](), [enterprise](), [genai](), [hdd](), [idc](), [inference](), [reports](), [research](), [retrieval](), [storage](), [synthetic-data]() ### AI overview Seagate and WD published separate studies indicating that AI is increasing enterprise storage requirements and extending data retention. Although their headline percentages differ because they asked different questions, both reports point to storage becoming a larger part of AI infrastructure planning alongside growing archive and retrieval needs. ### Source excerpt Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD's IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional The post Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck appeared first on StorageReview.com. ## AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories DevFeed: [AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories]() Original publisher: [Read original article]() Author: NVIDIA Writers Published: 2026-09-15T16:55:40Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [AI Infrastructure](), [Nvidia](), [DSX](), [Vera Rubin](), [Low-Latency Inference](), [NVLink](), [Conversational AI]() Tags: [ai](), [ai-infrastructure](), [dsx](), [inference](), [infra](), [latency](), [low-latency-inference](), [networking](), [nvidia](), [nvidia-blackwell](), [nvidia-dsx](), [nvidia-vera](), [nvidia-vera-rubin](), [nvlink]() ### AI overview NVIDIA's AI Infra Summit coverage describes collaborations and platform updates focused on improving AI factory efficiency. The article highlights Vera Rubin systems, DSX MaxLPS, Dynamo inference software, NVLink and networking technologies, including claims of up to 1.4x more tokens per megawatt through factory-wide power optimization. ### Source excerpt Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA, Tuesday spoke on AI factory efficiency at the AI Infra Summit, the Santa Clara Convention Center event that has morphed into a Coachella of infrastructure tech. Before a packed audience -- with more than 8,000 attendees this year, up from 3,500 last year -- [...] ## How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories DevFeed: [How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [Nvidia](), [AI Factory](), [AI Infrastructure](), [Hardware]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [data-center-cloud](), [hardware](), [industry](), [networking](), [networking-communications](), [nvidia](), [nvlink](), [resiliency](), [vera-rubin]() ### AI overview The article describes how NVIDIA NVLink 6 supports resiliency in large-scale AI factories. It explains that Vera Rubin NVL72 connects 72 Rubin GPUs into a single scale-up domain and outlines a multilayer approach using lossless networking, error correction, retry, flow control, and error containment to support continuous training and inference operations. ### Source excerpt For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster... ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## 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. ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## Temporal raises $550M at a $12.55B valuation as demand grows for reliable AI infrastructure DevFeed: [Temporal raises $550M at a $12.55B valuation as demand grows for reliable AI infrastructure]() Original publisher: [Read original article]() Author: Allanah Hughes Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [AI Infrastructure](), [reliability](), [Orchestration](), [AI Platform]() Tags: [agents](), [ai](), [ai-infrastructure](), [announcements](), [funding](), [infrastructure](), [orchestration](), [outage](), [reliability](), [series](), [systems]() ### AI overview Temporal announces a $550 million Series E funding round at a $12.55 billion valuation. The company says the funding will support reliable infrastructure for long-running AI agents and applications, including orchestration and recovery across systems. ### Source excerpt AI is raising the bar for reliability. See why Temporal's $550M Series E, backed by Lightspeed and others, is built to meet that demand. ## HeyGen x Google Cloud: Bringing Avatar IV to TPUs DevFeed: [HeyGen x Google Cloud: Bringing Avatar IV to TPUs]() Original publisher: [Read original article]() Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Google](), [Cloud](), [Artificial Intelligence](), [Optimization](), [AI Infrastructure](), [Transformer](), [PyTorch](), [Compiler](), [Streaming](), [real-time](), [Hardware](), [API]() Tags: [ai](), [ai-infrastructure](), [api](), [cloud](), [code](), [compiler](), [engineering](), [generation](), [google](), [hardware](), [model](), [optimization](), [performance](), [pytorch](), [real-time](), [streaming](), [time](), [tpu](), [transformers](), [video]() ### AI overview HeyGen and Google Cloud describe porting the 18B+ parameter Avatar IV talking-head video generation pipeline to an eight-chip Trillium TPU host. Using torchax, JAX, XLA, FSDP sharding, Ulysses sequence parallelism, and custom Pallas kernels, the team improved performance by 1.86x for real-time chunked streaming while preserving output quality through strict quality gates. ### Source excerpt HeyGen ported their 18B+ parameter Avatar IV video generation model to Google Cloud's Trillium (v6e) TPUs via torchax and XLA, utilizing FSDP and Ulysses sequence parallelism across an eight-chip mesh. To achieve a 1.86x speedup for real-time streaming, the engineering team pipelined exposed all-to-all collectives, aligned sparse attention block sizes to eliminate mask padding, and bypassed softmax serial dependencies using a precomputed Cauchy-Schwarz upper bound. These custom Pallas kernel and compiler optimizations were deployed only after passing rigorous two-tier quality gates to guarantee byte-identical or mathematically equivalent pixel outputs. ## Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack DevFeed: [Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:56:11Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Nemotron](), [Nvidia](), [Artificial Intelligence](), [Vera Rubin](), [AI Infrastructure](), [AI Platform](), [cuOpt](), [Complex Systems](), [Optimization]() Tags: [ai](), [ai-infrastructure](), [artificial-intelligence](), [complex-systems](), [cuopt](), [enterprise](), [models](), [nemotron](), [nvidia](), [open](), [optimization](), [supply-chain](), [systems](), [vera-rubin]() ### AI overview Palantir and NVIDIA have deployed a sovereign AI stack for supply chain operations, initially using NVIDIA's own Vera Rubin supply chain as the first customer. The system combines Nemotron open models with Palantir Foundry and AIP, NVIDIA NeMo Data Libraries, and cuOpt to support materials allocation, scenario planning, optimization, and risk detection while keeping final decisions with supply chain experts. ### Source excerpt Palantir and NVIDIA have built a sovereign AI stack for supply chain operations and are running it first inside NVIDIA's own supply chain, the one that has to line up 1.3 million parts for every Vera Rubin rack. The stack brings NVIDIA Nemotron open models into Palantir Foundry and its Artificial Intelligence Platform (AIP), grounded The post Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack appeared first on StorageReview.com. ## Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2 DevFeed: [Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:40:57Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Infrastructure](), [cloud-infrastructure](), [Cloud](), [data-processing](), [model-deployment]() Tags: [accelerators](), [ai](), [ai-infrastructure](), [cloud](), [cloud-storage](), [data](), [datasets](), [enterprise](), [inference](), [infrastructure](), [integration](), [performance](), [snapshots](), [storage]() ### AI overview Backblaze and WEKA validated a two-tier AI storage pipeline that uses WEKA NeuralMesh as the high-performance tier for GPU workloads and Backblaze B2 Cloud Storage as the capacity tier. Raw data, training sets, media, source files, checkpoints, and other assets can move between the tiers according to access needs. NeuralMesh's Snap-to-Object feature was also tested with B2 for storing consistent filesystem snapshots and supporting recovery. ### Source excerpt Backblaze and WEKA have validated their two platforms together for AI pipelines, pairing WEKA NeuralMesh as the performance tier that feeds GPUs with Backblaze B2 Cloud Storage as the capacity tier that holds everything else. The integration, sizing, tuning, and testing are already done, so an AI infrastructure team can deploy a proven two-tier layout The post Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2 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. ## From token consumer to token provider: Building your org's AI API DevFeed: [From token consumer to token provider: Building your org's AI API]() Original publisher: [Read original article]() Author: Markell Rawls Published: 2026-09-10T13:01:50Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Artificial Intelligence](), [API](), [API keys](), [AI Infrastructure](), [Security](), [NVIDIA DGX](), [vllm](), [Claude Code](), [hugging face](), [anthropic](), [OpenAI]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [api](), [api-keys](), [claude](), [claude-code](), [developer](), [gpu](), [hugging-face](), [openai](), [security](), [vllm]() ### AI overview The article describes how organizations can move from independently purchasing AI-provider access and distributing API keys toward centrally managing AI infrastructure and providing internal AI APIs. It highlights rising token costs, fragmented workloads, limited visibility, and security-audit difficulties. ### Source excerpt One of the biggest problems with AI right now is that it's expensive. If you've been anywhere near an enterprise IT budget in the last 2 years, then you already know that. Most companies that wanted to get AI into their workflows did the same thing: They signed up for a business account with an AI provider, handed out API keys, and started building. It made sense at the time because the models were good, the APIs were simple, and the alternative was standing up your own inference infrastructure, which nobody had bandwidth for. But then the bills started coming in. The post From token consumer to token provider: Building your org's AI API appeared first on Red Hat Developer. ## Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow DevFeed: [Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow]() Original publisher: [Read original article]() Author: Gireesh Sreedhar KP; Deepak Dhankani; Eash Sharma Published: 2026-09-09T03:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [AI Agent](), [databricks](), [AI Infrastructure](), [real-time]() Tags: [agents](), [ai](), [ai-engineering](), [architecture](), [best-practices](), [blog](), [company](), [customer](), [customers](), [data-science-and-ml](), [data-strategy](), [databricks](), [efficiency](), [engineering](), [evaluation](), [india](), [industries](), [operations](), [platform](), [product](), [production](), [real-time](), [retail-consumer-goods](), [scale](), [workflows]() ### AI overview This Databricks and MLflow case study describes how Zepto uses an evaluation-first, multi-agent AI system to operate customer support at more than 100,000 tickets per day. It focuses on the system architecture, evaluation framework, quality gate, and development and production loops used to improve reliability as volume, product categories, languages, and failure modes expand. ### Source excerpt Zepto's Push for Reliable, Real-Time Customer SupportZepto is one of India's fastest-growing... ## 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. ## NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral DevFeed: [NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-04T18:01:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [hugging face](), [Nvidia](), [AI Development](), [AI Infrastructure](), [Application Development](), [datasets](), [Cloud](), [Deployment](), [model-deployment](), [inference-providers]() Tags: [ai](), [ai-infrastructure](), [application-development](), [cloud](), [creators](), [datasets](), [deployment](), [enterprise](), [hugging-face](), [inference-providers](), [models](), [multi-cloud](), [nvidia](), [platforms]() ### AI overview NVIDIA has agreed to acquire Hugging Face for $12.93 billion, with plans to expand its infrastructure and AI development capabilities. Hugging Face is expected to retain its brand and operate as an open, hardware-neutral platform supporting models, datasets, applications, multiple clouds, accelerators, and inference providers. ### Source excerpt NVIDIA has agreed to acquire Hugging Face for $12.93 billion, a transaction that would extend the company's position from accelerated compute and AI infrastructure into one of the industry's most widely used platforms for open models, datasets, and application development. In an announcement published on the NVIDIA website, CEO Jensen Huang said the company plans The post NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral appeared first on StorageReview.com. ## ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer DevFeed: [ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-04T17:54:45Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [AI Factory](), [AI Infrastructure](), [AI Platform](), [NVIDIA Vera Rubin](), [NVIDIA DGX](), [NVIDIA Vera](), [MLOps](), [NVIDIA DSX](), [ai-governance](), [Server]() Tags: [2026](), [ai](), [ai-factory](), [ai-governance](), [ai-infrastructure](), [asus](), [enterprise](), [mlops](), [nvidia-dsx](), [nvidia-vera](), [nvl72](), [object-storage](), [platform](), [storage](), [vera-rubin-nvl72]() ### AI overview ASUS is broadening its AI infrastructure business from individual servers to a full AI factory platform covering accelerated computing, networking, storage, deployment, infrastructure management, MLOps, and governance. The article describes NVIDIA DSX-based planning, ASUS deployment and management tools, governance for AI services and autonomous agents, and server systems based on NVIDIA Vera Rubin hardware. ### Source excerpt ASUS is expanding its role in AI infrastructure, moving from individual AI servers to platforms for building, deploying, and operating entire AI factories. At AI Tech 2026 in Seoul, the company laid out a broader strategy that brings accelerated computing, networking, storage, deployment software, infrastructure management, and AI governance together under one platform. That puts The post ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer 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. ## VAST Data CrowdStrike Integration Goes Live: Native Falcon Sensor Now, Next-Gen SIEM and AIDR in Preview DevFeed: [VAST Data CrowdStrike Integration Goes Live: Native Falcon Sensor Now, Next-Gen SIEM and AIDR in Preview]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-01T15:37:31Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Cybersecurity](), [Securing AI](), [AI Infrastructure](), [SIEM, Security](), [SIEM, Security, Observability](), [prompt injection](), [Jailbreak](), [pii](), [Retrieval Augmented Generation (RAG)](), [Nvidia]() Tags: [ai](), [ai-infrastructure](), [ai-security](), [cybersecurity](), [enterprise](), [infrastructure](), [nvidia](), [pii](), [prompt-injection](), [security]() ### AI overview VAST Data and CrowdStrike have launched an integration that embeds Falcon security capabilities into VAST AI storage infrastructure, data pipelines, and production AI workloads. The integration supports native Falcon sensors, sends VAST audit telemetry to CrowdStrike Next-Gen SIEM, inspects data in flight with Falcon AIDR, helps identify PII before downstream use, and detects prompt injection and jailbreak attempts during model interactions. ### Source excerpt VAST Data and CrowdStrike are turning the AI security partnership they announced at VAST Forward in February into a shipping product, detailing a multi-layered integration that embeds enterprise-grade cybersecurity directly into AI storage infrastructure, data pipelines, and production AI workloads. By combining the VAST AI Operating System with the CrowdStrike Falcon platform, the collaboration addresses The post VAST Data CrowdStrike Integration Goes Live: Native Falcon Sensor Now, Next-Gen SIEM and AIDR in Preview appeared first on StorageReview.com. ## The invisible heartbeat of our networks DevFeed: [The invisible heartbeat of our networks]() Original publisher: [Read original article]() Author: Luca Cicchelli Published: 2026-08-31T23:03:47Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Networks](), [distributed-systems](), [Critical Infrastructure](), [Security](), [5G](), [Cybersecurity](), [cloud-infrastructure](), [AI Infrastructure]() Tags: [5g](), [ai](), [ai-infrastructure](), [critical-infrastructure](), [cybersecurity](), [data](), [distributed-systems](), [engineering](), [gnss](), [gps](), [guest-post](), [hardware](), [network](), [networking](), [networks](), [outage](), [outages](), [post](), [servers](), [systems](), [tech-matters](), [time](), [transactions]() ### AI overview The article examines how accurate time synchronization underpins modern networks and distributed systems. It uses the Telstra outage to show how misaligned time servers can disrupt communications and rail services, and discusses resilient time sources, terrestrial backups, local atomic clocks, and synchronization requirements for finance, 5G, cloud, and AI infrastructure. ### Source excerpt Guest Post: The recent Telstra outage highlighted a critical but often overlooked dependency in modern networks: Accurate time synchronization. ## How I turned Claude into a self-improving PM assistant | Daniel Blum (PM, Melio) DevFeed: [How I turned Claude into a self-improving PM assistant | Daniel Blum (PM, Melio)]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-08-31T12:04:18Z Content type: article Language: en Sources: [Lenny's Newsletter]() Topics: [Claude](), [Notion](), [Slack](), [email](), [Artificial Intelligence](), [Automation](), [AI Infrastructure](), [User experience (UX)]() Tags: [agent-orchestration](), [automation](), [claude](), [email](), [notion](), [slack](), [ux]() ### AI overview A podcast episode in which Daniel Blum explains how he uses Claude and Cowork to manage Notion, Slack, and email workflows, automate weekly preparation, learn internal terminology, and identify improvements to his system. He also discusses scaling the setup to Melio's PM team and the remaining limitations. ### Source excerpt Listen now | 🎙 PM Daniel Blum walks through the exact Cowork and Claude setup that manages his Notion, tracks his tasks across Slack and email, and self-improves every week without him lifting a finger ## 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,... ## NVIDIA NVLink Fusion Expands With NVHBM Custom High-Bandwidth Memory DevFeed: [NVIDIA NVLink Fusion Expands With NVHBM Custom High-Bandwidth Memory]() Original publisher: [Read original article]() Author: Jesse Clayton Published: 2026-08-26T21:05:30Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [NVLink](), [AI Infrastructure](), [Nvidia](), [networking](), [Amazon Web Services](), [amazon]() Tags: [ai-infrastructure](), [amazon](), [aws](), [hardware](), [infrastructure](), [networking](), [nvidia](), [nvlink](), [performance](), [xpu]() ### AI overview NVIDIA expands NVLink Fusion with NVHBM, a high-bandwidth memory technology for custom AI infrastructure. By moving the memory controller into the HBM base die, NVHBM is designed to provide up to 30% greater memory bandwidth, 15% lower HBM power consumption, and up to 25% more XPU compute-die area than standard HBM4E. Amazon's Annapurna Labs will be the first memory partner to work with NVIDIA on the technology, alongside collaboration on NVLink scale-up architecture for future AWS Trainium systems. ### Source excerpt The next wave of AI is placing new demands on infrastructure. As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a unified system. To help hyperscalers and AI innovators build the next generation [...] ## 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. [Next page]()