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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. Solving this problem requires a robust and high-speed network infrastructure as well as efficient data transfer protocols and algorithms. requires revisiting trade-offs made for other types of workloads.

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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.

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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. The other cluster features an NVIDIA Quantum2 InfiniBand fabric.

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Top Tips for Debugging and Optimizing NVIDIA Networking Performance

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In today’s high-speed networking world, optimizing and troubleshooting performance is crucial, especially with high-performance equipment like NVIDIA Infiniband switches. Whether you’re a data center admin or network engineer, mastering effective techniques is key.