On-Demand Videos
In this talk, Ojus Save walks you through a demo of how to build AI applications on Zoom. This demo shows you an AI agent that receives transcript data from RTMS and then decides if it has to create action items based on the transcripts that are received.
In this talk, Sandeep Joshi, , Senior Manager at NVIDIA, shares how to accelerate the data access between GPU and storage for AI. Sandeep will dive into two options: CPU- initiated GPUDirect Storage and GPU-initiated SCADA.
Bin Fan, VP of Technology at Alluxio, introduces how Alluxio, a software layer transparently sits between application and S3 (or other object stores), provides sub-ms time to first byte (TTFB) solution, with up to 45x lower latency.
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This video is originally published on TechArena.
At NVIDIA GTC 2025, Bin Fan from Alluxio and Scott Shadley from Solidigm tackled the growing need for decoupled storage and compute in AI infrastructure. They explained how Alluxio's caching layer enables fast, scalable and reliable infrastructure, accelerating AI training and inferencing seamlessly across regions and platforms.
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.
In this talk, Xu Ning from Snap provides a comprehensive overview of the unique challenges in building and scaling recommendation systems compared to LLM applications.
Join Chongxiao Cao from Uber's Michelangelo training team as he walks you through Uber's approach to optimizing LLM training and fine-tuning workflows.
In this talk, Bin Fan shares his insights on data access challenges in ML applications, with particular emphasis on how Alluxio's distributed caching helps bridge the gap between storage and compute in preprocessing, pretraining and inference.
Watch this video to gain insights onhow Uber manages its Generative AI Gateway, which powers all generative AI applications across the company.
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
Ready to optimize your AI infra strategy? Watch this on-demand video, where Bin Fan, VP of Technology at Alluxio, will guide you through how to balance cost & performance for GPU/CPU workloads.
LLM inference can be huge, particularly, with long contexts. In this on-demand video, Junchen Jiang, Assistant Professor at University of Chicago, presents a 10x solution for long contexts inference: an easy-to-deploy stack over multiple vLLM engines with tailored KV-cache backend.
You won't want to miss this talk presented by Robert Nishihara, Co-Founder of Anyscale, which is packed with insights on using Ray to conquer the last-mile challenges in AI deployment.
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 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.
In this talk, Zhe Zhang (NVIDIA, ex-Anyscale) introduced Ray and its applications in the LLM and multi-modal AI era. He shared his perspective on ML infrastructure, noting that it presents more unstructured challenges, and recommended using Ray and Alluxio as solutions for increasingly data-intensive multi-modal AI workloads.