As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for robust data infrastructures tailored for large language model (LLM) training and inference in the cloud. To effectively leverage the breakthroughs in LLM, organizations must ensure low latency, high concurrency, and scalability in production environments.
In this Alluxio-hosted webinar, Shouwei presented on the design and implementation of a distributed caching system that addresses the I/O challenges of LLM training and inference. He explored the unique requirements of data access patterns and offer practical best practices for optimizing the data pipeline through distributed caching in the cloud. The session featured insights from real-world examples, such as Microsoft, Tencent, and Zhihu, as well as from the open-source community. Watch this recording to get a deeper understanding of how to harness scalable, efficient, and robust data infrastructures for LLM training and inference.
As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for robust data infrastructures tailored for large language model (LLM) training and inference in the cloud. To effectively leverage the breakthroughs in LLM, organizations must ensure low latency, high concurrency, and scalability in production environments.
In this Alluxio-hosted webinar, Shouwei presented on the design and implementation of a distributed caching system that addresses the I/O challenges of LLM training and inference. He explored the unique requirements of data access patterns and offer practical best practices for optimizing the data pipeline through distributed caching in the cloud. The session featured insights from real-world examples, such as Microsoft, Tencent, and Zhihu, as well as from the open-source community. Watch this recording to get a deeper understanding of how to harness scalable, efficient, and robust data infrastructures for LLM training and inference.
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As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for robust data infrastructures tailored for large language model (LLM) training and inference in the cloud. To effectively leverage the breakthroughs in LLM, organizations must ensure low latency, high concurrency, and scalability in production environments.
In this Alluxio-hosted webinar, Shouwei presented on the design and implementation of a distributed caching system that addresses the I/O challenges of LLM training and inference. He explored the unique requirements of data access patterns and offer practical best practices for optimizing the data pipeline through distributed caching in the cloud. The session featured insights from real-world examples, such as Microsoft, Tencent, and Zhihu, as well as from the open-source community. Watch this recording to get a deeper understanding of how to harness scalable, efficient, and robust data infrastructures for LLM training and inference.
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Deepseek’s recent announcement of the Fire-flyer File System (3FS) has sparked excitement across the AI infra community, promising a breakthrough in how machine learning models access and process data.
In this webinar, an expert in distributed systems and AI infrastructure will take you inside Deepseek 3FS, the purpose-built file system for handling large files and high-bandwidth workloads. We’ll break down how 3FS optimizes data access and speeds up AI workloads as well as the design tradeoffs made to maximize throughput for AI workloads.
This webinar you’ll learn about how 3FS works under the hood, including:
✅ The system architecture
✅ Core software components
✅ Read/write flows
✅ Data distribution/placement algorithms
✅ Cluster/node management and disaster recovery
Whether you’re an AI researcher, ML engineer, or infrastructure architect, this deep dive will give you the technical insights you need to determine if 3FS is the right solution for you.