# Networking & Traffic Published articles for Networking & Traffic. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## MTIA 300: Meta's First Training Chip with Built-in NICs and Communication-Offloading Engines DevFeed: [MTIA 300: Meta's First Training Chip with Built-in NICs and Communication-Offloading Engines]() Original publisher: [Read original article]() Author: Rajiv Krishnamurthy; Wes Bland Published: 2026-08-24T17:45:52Z Content type: article Language: en Sources: [Engineering at Meta]() Topics: [recommendation systems](), [Language models]() Tags: [chip-design](), [communication](), [data-infrastructure](), [devinfra](), [gpu](), [hardware](), [inference](), [infrastructure](), [meta](), [networking-traffic](), [performance](), [production-engineering](), [training]() ### AI overview Meta describes MTIA 300, an in-house accelerator for training ranking and recommendation models, with built-in network chiplets and a co-designed HCCL communication library. The design targets communication-heavy distributed training by integrating RDMA NICs into the chip package and offloading communication work. ### Source excerpt MTIA 300 is the first of Meta's family of in-house training and inference accelerators optimized for training ranking and recommendation models. We're sharing how MTIA 300's built-in NIC chiplets allow it to meet the communication needs associated with training recommendation models with superior performance over general-purpose GPUs. By co-designing MTIA's communication library, HCCL, alongside the [...] Read More... The post MTIA 300: Meta's First Training Chip with Built-in NICs and Communication-Offloading Engines appeared first on Engineering at Meta. ## RCCLX: Innovating GPU Communications on AMD Platforms DevFeed: [RCCLX: Innovating GPU Communications on AMD Platforms]() Original publisher: [Read original article]() Author: Sudharssun Subramanian; Subodh Iyengar; Cen Zhao; Srinath Bayareddy; James Hongyi Zeng Published: 2026-02-24T21:30:54Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [GPU](), [Meta](), [communications](), [AI Models](), [Latency](), [Inference](), [Large Language Model](), [Transformer]() Tags: [accelerate](), [ai](), [ai-models](), [ai-research](), [communications](), [data-center-engineering](), [gpu](), [inference](), [large-language-model](), [latency](), [layer](), [meta](), [ml-applications](), [networking-traffic](), [open-source](), [parallelism]() ### AI overview Meta describes the initial open-source release of RCCLX, an enhanced version of RCCL for AMD platforms integrated with Torchcomms. The article presents Direct Data Access algorithms and Low Precision Collectives, including approaches intended to reduce communication latency during large language model inference. ### Source excerpt We are open-sourcing the initial version of RCCLX - an enhanced version of RCCL that we developed and tested on Meta's internal workloads. RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend. Communication patterns for AI models are constantly evolving, as are hardware [...] Read More... The post RCCLX: Innovating GPU Communications on AMD Platforms appeared first on Engineering at Meta. ## Meta's Infrastructure Evolution and the Advent of AI DevFeed: [Meta's Infrastructure Evolution and the Advent of AI]() Original publisher: [Read original article]() Author: Yee Jiun Song; Kaushik Veeraraghavan Published: 2025-09-29T13:00:15Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [Meta](), [Artificial Intelligence](), [Hardware](), [Network](), [cloud-infrastructure](), [Terraform]() Tags: [ai](), [ai-research](), [apache](), [data-center-engineering](), [data-centers](), [data-infrastructure](), [data-management](), [database](), [devinfra](), [hardware](), [infrastructure](), [lamp](), [linux](), [meta](), [ml-applications](), [mysql](), [network](), [networking-traffic](), [open-source](), [production-engineering](), [scale]() ### AI overview Meta describes how its infrastructure evolved from a small university-focused social network into a globally networked operation serving more than 3.4 billion people. The article explains that AI has changed infrastructure-scaling assumptions and requires innovation across hardware, software, networks, and data centers, while outlining earlier database, caching, social graph, ranking, and photo-service scaling work. ### Source excerpt Over the past 21 years, Meta has grown exponentially from a small social network connecting a few thousand people in a handful of universities in the U.S. into several apps and novel hardware products that serve over 3.4 billion people throughout the world. Our infrastructure has evolved significantly over the years, growing from a [...] Read More... The post Meta's Infrastructure Evolution and the Advent of AI appeared first on Engineering at Meta.