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A RoCE network for distributed AI training at scale

Engineering at Meta

AI networks play an important role in interconnecting tens of thousands of GPUs together, forming the foundational infrastructure for training, enabling large models with hundreds of billions of parameters such as LLAMA 3.1 Distributed training, in particular, imposes the most significant strain on data center networking infrastructure.

Network 132
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Critically Engaging With Models

Mathias Verraes

First, well discuss the hierarchical, social network, and the value creation models. Then, well examine three organisational models that are specific to software development: the Spotify Model, the Agile Fluency Model, and Team Topologies. Hierarchies and Networks A traditional way of looking at organisations is the hierarchical model.

Topology 162
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How Meta trains large language models at scale

Engineering at Meta

Supporting GenAI at scale has meant rethinking how our software, hardware, and network infrastructure come together. 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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Maintaining large-scale AI capacity at Meta

Engineering at Meta

Meta runs different types of backend networks, topologies, and training jobs that have tight dependencies between software and hardware components. Basically, any kind of operation that updates or verifies software and firmware components in the clusters, including the networking path. And what do we mean by maintaining?

Fashion 138
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Building Meta’s GenAI Infrastructure

Engineering at Meta

We are sharing details on the hardware, network, storage, design, performance, and software that help us extract high throughput and reliability for various AI workloads. Network At Meta, we handle hundreds of trillions of AI model executions per day. We use this cluster design for Llama 3 training.

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SDN and Self-Driving Networks

Kentik

Traffic Intelligence is the Key to Effective Network Automation. Reading the tech press, one might understandably conclude that software defined networks (SDN) are “eating the world” (to borrow from Marc Andreesen). Meanwhile the hype of SDN goes far beyond automating and simplifying network provisioning. Where Da Brain?

Network 40
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Journey to Event Driven – Part 4: Four Pillars of Event Streaming Microservices

Confluent

In the next level down, they can be mapped to the underlying broker infrastructure metrics, such as consumer lag, throughput per topic and partition hotspots, in addition to operating system metrics like CPU, network I/O, disk I/O, load average, etc. Control plane. Unlike batch systems, event streaming applications continuously process data.