Events

Tuesday April 1, 11am PT

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.

‍Speaker Bio

Stephen Pu, Staff Software Engineer at Alluxio, has over 15 years of experience in software R&D for data centers and distributed storage systems. He has been involved in the core product development and design of large-scale distributed data platforms at IBM, HPE, and Fortinet. Stephen has deep expertise in the performance, scalability, and reliability of distributed data systems, with a strong understanding of architectural design in these areas.

‍

Join us to learn about the latest release of Alluxio Enterprise AI. In this webinar, we’ll provide an overview of the new features and capabilities of Alluxio Enterprise AI, built to accelerate AI workloads and maximize GPU utilization.

Key highlights include:

• New caching mode accelerates AI checkpoints
• Advanced cache eviction policies provide fine-grained control
• Python SDK integrations enhance AI framework compatibility
• A demo of Alluxio accelerating AI training workloads in AWS

In the rapidly evolving landscape of AI and machine learning, Platform and Data Infrastructure Teams face critical challenges in building and managing large-scale AI platforms. Performance bottlenecks, scalability of the platform, and scarcity of GPUs pose significant challenges in supporting large-scale model training and serving.

In this talk, we will introduce how Alluxio helps Platform and Data Infrastructure teams deliver faster, more scalable platforms to ML Engineering teams developing and training AI models. Alluxio’s highly-distributed cache accelerates AI workloads by eliminating data loading bottlenecks and maximizing GPU utilization. Customers report up to 4x faster training performance with high-speed access to petabytes of data spread across billions of files regardless of persistent storage type or proximity to GPU clusters. Alluxio’s architecture lowers data infrastructure costs, increases GPU utilization, and enables workload portability for navigating GPU scarcity challenges.

‍