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.
Video:
Presentation slides:
Videos:
Presentation Slides:
Complete the form below to access the full overview:
.png)
Videos
Pratik Mishra delivered insights on architecting scalable, deployable, and resilient AI infrastructure at scale. His discussion on fault tolerance, checkpoint optimization, and the democratization of AI compute through AMD's open ecosystem resonated strongly with the challenges teams face in production ML deployments.
In this talk, Bin Fan, VP of Technology at Alluxio, presents on building tiered architectures that bring sub-millisecond latency to S3-based workloads. The comparison showing Alluxio's 45x performance improvement over S3 Standard and 5x over S3 Express One Zone demonstrated the critical role the performance & caching layer plays in modern AI infrastructure.
In this talk, Greg Lindstrom shared how Blackout Power Trading achieved double-digit millisecond offline feature store performance using Alluxio, a game-changer for real-time power trading where every millisecond counts. The 60x latency reduction for inference queries was particularly impressive.