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A RoCE network for distributed AI training at scale

Engineering at Meta

Distributed training, in particular, imposes the most significant strain on data center networking infrastructure. Constructing a reliable, high-performance network infrastructure capable of accommodating this burgeoning demand necessitates a reevaluation of data center network design.

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Building Meta’s GenAI Infrastructure

Engineering at Meta

Today, we’re sharing details on two versions of our 24,576-GPU data center scale cluster at Meta. Custom designing much of our own hardware, software, and network fabrics allows us to optimize the end-to-end experience for our AI researchers while ensuring our data centers operate efficiently.

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How Meta trains large language models at scale

Engineering at Meta

This means we need to regularly checkpoint our training state and efficiently store and retrieve training data. Optimal connectivity between GPUs: Large-scale model training involves transferring vast amounts of data between GPUs in a synchronized fashion. requires revisiting trade-offs made for other types of workloads.

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Egnyte Architecture: Lessons learned in building and scaling a multi petabyte content platform

High Scalability

Egnyte Connect platform employs 3 data centers to fulfill requests from millions of users across the world. To add elasticity, reliability and durability, these data centers are connected to Google Cloud platform using high speed, secure Google Interconnect network. On prem data processing. Data interdependence.