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Systems and Networking

Keynote from Microsoft

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Systems and Networking

10x Backbone: Scaling Backbone Connectivity to Serve AI Demands

In this presentation we will share details about Meta’s Backbone Network, its recent developments, and the journey to support increasing demands that our existing and new AI workloads place on the network. New technologies and designs to address 10x scaling needs in the are discussed as well as how some of these same principles are […]
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Systems and Networking

Track 2 – Live Q&A Session #2

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Systems and Networking

Scaling Llama4 Training to 100K

Llama 4’s pre-training scale is growing exponentially, with 100K GPUs used, a 6x increase from its predecessor. Initializing training takes longer, and failure probability increases with larger scale. Training throughput aka Effective Training time degrades significantly as a result. To address these challenges, researchers are experimenting in parallel for faster initialization of large scale jobs, […]
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Systems and Networking

Performance Optimizations at 100K+ Scale

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Systems and Networking

Track 2 – Live Q&A Session #1

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Systems and Networking

PyTorch Symmetric Memory: A New Paradigm for Programming Distributed AI

Recent model advancements have highlighted the need for customized communication. In response, PyTorch introduces Symmetric Memory, a distributed programming model that creates a global address space for data spanning multiple GPUs’ memory. In this talk, we will demonstrate how developers can author their own communication kernels at the device level. Additionally, we will show how […]
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Systems and Networking

Enhancing Runtime Reliability in LLM Training via Fine-Grained Observability

As large language model (LLM) training scales across tens of thousands of GPUs, ensuring runtime reliability becomes both more challenging and more critical for maintaining efficiency. This talk explores how fine-grained observability can substantially enhance reliability in LLM training at scale. First, we discuss automated methods for detecting faulty machines by leveraging distinctive monitoring metric […]
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Systems and Networking

Inference Deployments and Comms Implication

This talk addresses the challenges and solutions for scaling large language model (LLM) inference to support up to 1 billion monthly active users across platforms for Meta AI, focusing on compute-bound prefill and memory-bound decode stages. Key challenges include the quadratic scaling of attention operations with sequence length and the linear growth of the KV […]
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Systems and Networking

Track 1 – Live Q&A Session #2

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