# InfiniBand Published articles for InfiniBand. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University DevFeed: [VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T10:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [High-Performance Computing](), [Data Infrastructure](), [InfiniBand](), [Post-quantum cryptography](), [Deployment](), [Training AI Models](), [Hardware](), [Low Latency](), [Quantum Computing]() Tags: [ai](), [data-infrastructure](), [deployment](), [enterprise](), [enterprise-storage](), [hardware](), [high-performance-computing](), [infiniband](), [low-latency](), [post-quantum-cryptography-pqc](), [quantum](), [storage]() ### AI overview VDURA has moved its storage platform at New Mexico State University into full production to support AI and high-performance computing research. The deployment combines NVMe flash and high-density HDD tiers through a global namespace over InfiniBand, allowing separate scaling of performance and capacity. It also supports NMSU's post-quantum cryptography research and large-scale data pipeline projects. ### Source excerpt VDURA has completed the deployment of its data platform at New Mexico State University (NMSU), moving the system into full production. The infrastructure is designed to serve the university's research community with a high-durability, high-throughput storage environment tailored specifically for artificial intelligence and high-performance computing (HPC) workloads. NMSU, which holds Carnegie R1 status and manages The post VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University appeared first on StorageReview.com. ## 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,... ## How to Choose Full-Stack Observability for NVIDIA AI Factories DevFeed: [How to Choose Full-Stack Observability for NVIDIA AI Factories]() Original publisher: [Read original article]() Author: Jorge Cardoso Published: 2026-08-12T16:13:47Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [observability](), [AI Infrastructure](), [NVIDIA DGX](), [InfiniBand](), [telemetry](), [dashboards](), [NCCL](), [Monitoring]() Tags: [ai-factory](), [ai-infrastructure](), [data-center-cloud](), [distributed-training](), [featured](), [infiniband](), [infrastructure](), [monitoring](), [nccl](), [networking](), [networking-communications](), [nvidia-dgx](), [observability](), [operations](), [orchestration](), [performance](), [storage](), [systems](), [telemetry]() ### AI overview A practical guide to choosing a full-stack observability strategy for NVIDIA AI infrastructure. It explains how to connect telemetry across compute, networking, storage, orchestration, and applications, using an InfiniBand gray-failure example to show how degraded hardware and NCCL collective-operation delays can reduce distributed-training throughput. ### Source excerpt AI infrastructure spans multiple layers, from compute and networking to storage, orchestration, and applications. When performance degrades, identifying the... ## NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure DevFeed: [NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-30T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [debugging](), [Processes]() Tags: [ai](), [ai-infrastructure](), [benchmarks](), [blackwell](), [cloud](), [cloud-services](), [cpu](), [data-center-cloud](), [debugging](), [developer-tools-techniques](), [dgx-cloud](), [diagnostics](), [distributed-training](), [featured](), [gb200](), [gb300-nvl72](), [grace-cpu](), [hardware](), [hopper](), [infiniband](), [infrastructure](), [kernel](), [linux](), [nvidia](), [nvl72](), [performance](), [training]() ### AI overview A troubleshooting guide for closing AI-training throughput gaps between NVIDIA reference architectures and partner clusters. It covers configuration and installation issues across memory management, CPU power and NUMA placement, NCCL queue-pair concurrency, and hardware setup. ### Source excerpt Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We... ## NVIDIA NVLink: The Scale-Up Network for AI Factories DevFeed: [NVIDIA NVLink: The Scale-Up Network for AI Factories]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-20T15:46:28Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [AI Factory](), [AI Infrastructure](), [GPU](), [networking](), [Artificial Intelligence](), [Nvidia](), [datacenter](), [Latency](), [Mixture of Experts (MoE)](), [Inference Performance](), [Low Latency]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-factory](), [ai-inference](), [ai-infrastructure](), [collective](), [communication](), [compute](), [data-center](), [featured](), [gpu](), [inference](), [infiniband](), [latency](), [low-latency](), [mixture-of-experts-moe](), [networking](), [nvidia](), [nvlink](), [production](), [scale](), [spectrum-ethernet](), [spectrum-x](), [speed](), [systems](), [vera-rubin]() ### AI overview NVIDIA NVLink is presented as a scale-up networking fabric for AI factories. It provides high-bandwidth, low-latency GPU-to-GPU communication for large AI inference, training, and parallel-computing workloads, with collective-operation acceleration and rack-level resiliency. ### Source excerpt The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute... ## Нейро сети для самых маленьких. Часть первая (которая после нулевой). Удобство в прокрустовом ложе оптимизации DevFeed: [Нейро сети для самых маленьких. Часть первая (которая после нулевой). Удобство в прокрустовом ложе оптимизации]() Original publisher: [Read original article]() Author: eucariot (Яндекс, Yandex Cloud & Yandex Infrastructure) Published: 2026-07-01T07:00:06Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [High-Performance Computing](), [GPU](), [InfiniBand](), [NVLink](), [Linux]() Tags: [ethernet](), [gpu](), [gpudirect-rdma](), [high-performance-computing](), [infiniband](), [linux](), [nvidia](), [nvlink](), [performance](), [rdma](), [roce](), [tcp](), [zero-copy]() ### AI overview This introductory article in a series explains the infrastructure used to train and run neural networks and for high-performance computing. It surveys specialized technologies including GPUs and TPUs, RDMA, kernel bypass, NVLink, InfiniBand, and RoCE, arguing that specialized solutions can outperform and cost less than a generic Linux and Ethernet/IP stack at scale. ### Source excerpt Это первая (после нулевой) статья из серии Нейро сети для самых маленьких, в которой мы разбираем инфраструктуру для запуска нейронных сетей. Для обучения и инференса нейросетей и для любых видов High Performance Computing используются специализированные технологии: GPU/TPU, RDMA, Kernel bypass, NVLink, InfiniBand, RoCE и другие. Про некоторые из них большинство только что-то слышали, но сталкиваться с ними не приходилось. Нельзя просто взять ванильный стек Linux, воткнуть в него 400Gb Ethernet+IP и получить рабочее решение. Почему? Потому что общее решение на масштабе в большинстве случаев проигрывает специализированным как в скорости, так и в стоимости. Как бы странно последнее ни звучало. Читать далее ## How Shopify uses SkyPilot to route machine-learning workloads across multi-cloud GPU clusters DevFeed: [How Shopify uses SkyPilot to route machine-learning workloads across multi-cloud GPU clusters]() Original publisher: [Read original article]() Author: Javier Moreno Published: 2026-01-26T14:49:55Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [skypilot](), [Cloud](), [Machine learning](), [InfiniBand]() Tags: [cloud](), [development](), [distributed-training](), [infiniband](), [kubernetes](), [kubernetes-clusters](), [multi-cloud](), [open-source](), [shopify](), [skypilot](), [yaml]() ### AI overview Shopify describes using SkyPilot to run machine-learning workloads across existing Kubernetes clusters on multiple clouds. A custom plugin routes jobs based on requested hardware and workload needs, while supporting multi-team management, cost tracking, fair scheduling, and policy enforcement. ### Source excerpt GPUs are annoying. Shopify uses SkyPilot to make them less so: one YAML file, multiple clouds, clean development ergonomics. ## Locks & Condition Variables - Latency Impact DevFeed: [Locks & Condition Variables - Latency Impact]() Original publisher: [Read original article]() Author: Martin Thompson (noreply@blogger.com) Published: 2011-11-05T13:52:00Z Content type: tutorial Language: en Sources: [Mechanical Sympathy]() Topics: [Concurrency](), [Latency](), [Concurrent Programming](), [Java](), [Low Latency](), [Linux]() Tags: [infiniband](), [java](), [latency](), [linux](), [locks](), [low-latency](), [performance](), [thread](), [threads](), [windows]() ### AI overview This article measures the latency impact of using locks and condition variables to pass control between two Java threads. It reports that kernel arbitration and thread scheduling add substantially more latency than signaling with memory barriers, and that allowing the operating system to schedule threads across different cores can hurt low-latency performance through cache pollution. ### Source excerpt In a previous article on Inter-Thread Latency I showed how it is possible to signal a state change between 2 threads with less than 50ns of latency. To many developers, writing concurrent code using locks is a scary experience. Writing concurrent code using lock-free algorithms, i.e. algorithms that rely on the use of memory barriers and an intimate understanding of the underlying memory models, can be totally terrifying. To me lock-free / non-blocking algorithms are like playing with explosives or corrosive chemicals, if you do not understand what you are doing, or show the ultimate respect, then very bad things can, and most likely will, happen! In this article, I'd like to illustrate the impact of using locks and the resulting latency they can impose on your designs. I want to use a very similar algorithm to that used in my previous inter-thread latency article to illustrate the ping-pong effect of handing control back and forth between 2 threads. In this case, rather than using a couple of volatile variables, I will employ a pair of condition variables to signal a state change so control can be passed back and forth. The Code import java.util.concurrent.locks.Condition; import java.util.concurrent.locks.Lock; import java.util.concurrent.locks.ReentrantLock; import static java.lang.System.out; public final class LockedSignallingLatency { private static final int ITERATIONS = 10 * 1000 * 1000; private static final Lock lock = new ReentrantLock(); private static final Condition sendCondition = lock.newCondition(); private static final Condition echoCondition = lock.newCondition(); private static long sendValue = -1L; private static long echoValue = -1L; public static void main(final String[] args) throws Exception { final Thread sendThread = new Thread(new SendRunner()); final Thread echoThread = new Thread(new EchoRunner()); final long start = System.nanoTime(); echoThread.start(); sendThread.start(); sendThread.join(); echoThread.join(); final long duration = Sys