# InfiniBand

Industry-standard switched-fabric interconnect architecture for connecting servers, storage, and other computing systems.

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## 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](<https://devfeed.tech/articles/fs-pairs-1-6t-scale-out-optics-with-500-km-coherent-modules-and-a-handheld-toolkit-for-ai-fabrics-17434.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/fs-pairs-1-6t-scale-out-optics-with-500-km-coherent-modules-and-a-handheld-toolkit-for-ai-fabrics>)

Author: Harold Fritts

Published: 2026-09-14T17:34:25Z

Content type: news

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [Networks](<https://devfeed.tech/topics/networks.md>), [Ethernet](<https://devfeed.tech/topics/ethernet.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [ethernet](<https://devfeed.tech/tags/ethernet.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networks](<https://devfeed.tech/tags/networks.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>)

### 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](<https://devfeed.tech/articles/vdura-deploys-high-performance-storage-platform-for-ai-and-hpc-at-new-mexico-state-university-12380.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/vdura-deploys-high-performance-storage-platform-for-ai-and-hpc-at-new-mexico-state-university>)

Author: Harold Fritts

Published: 2026-09-02T10:00:00Z

Content type: news

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [High-Performance Computing](<https://devfeed.tech/topics/high-performance-computing.md>), [Data Infrastructure](<https://devfeed.tech/topics/data-infrastructure.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [Post-quantum cryptography](<https://devfeed.tech/topics/post-quantum-cryptography.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [enterprise-storage](<https://devfeed.tech/tags/enterprise-storage.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [high-performance-computing](<https://devfeed.tech/tags/high-performance-computing.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [post-quantum-cryptography-pqc](<https://devfeed.tech/tags/post-quantum-cryptography-pqc.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### 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.

## How to Choose Full-Stack Observability for NVIDIA AI Factories

DevFeed: [How to Choose Full-Stack Observability for NVIDIA AI Factories](<https://devfeed.tech/articles/how-to-choose-full-stack-observability-for-nvidia-ai-factories-6847.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-to-choose-full-stack-observability-for-nvidia-ai-factories/>)

Author: Jorge Cardoso

Published: 2026-08-12T16:13:47Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [observability](<https://devfeed.tech/topics/observability.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [featured](<https://devfeed.tech/tags/featured.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [observability](<https://devfeed.tech/tags/observability.md>), [operations](<https://devfeed.tech/tags/operations.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [storage](<https://devfeed.tech/tags/storage.md>), [systems](<https://devfeed.tech/tags/systems.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

### 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...

## 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](<https://devfeed.tech/articles/article-24859.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1047072/>)

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](<https://devfeed.tech/sources/source.md>)

Topics: [High-Performance Computing](<https://devfeed.tech/topics/high-performance-computing.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [Linux](<https://devfeed.tech/topics/linux.md>)

Tags: [ethernet](<https://devfeed.tech/tags/ethernet.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpudirect-rdma](<https://devfeed.tech/tags/gpudirect-rdma.md>), [high-performance-computing](<https://devfeed.tech/tags/high-performance-computing.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [linux](<https://devfeed.tech/tags/linux.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rdma](<https://devfeed.tech/tags/rdma.md>), [roce](<https://devfeed.tech/tags/roce.md>), [tcp](<https://devfeed.tech/tags/tcp.md>), [zero-copy](<https://devfeed.tech/tags/zero-copy.md>)

### 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