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In this session, Pratik Mishra delivers 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.
Blackout Power Trading, a private capital commodity trading fund specializing in North American power markets, leverages Alluxio's low-latency distributed caching platform to achieve multi-join double-digit millisecond latency offline feature store performance while maintaining the cost and durability benefits of Amazon S3 for persistent data storage.
Alluxio's strong Q2 featured Enterprise AI 3.7 launch with sub-millisecond latency (45× faster than S3 Standard), 50%+ customer growth including Salesforce and Geely, and MLPerf Storage v2.0 results showing 99%+ GPU utilization, positioning the company as a leader in maximizing AI infrastructure ROI.
Amazon S3 has become the de facto cloud hard drive—scalable, durable, and cost-effective for ETL, OLAP, and archival workloads.
However, as workloads shift toward training, inference, and agentic AI, S3's original assumptions begin to show limits.
Alluxio takes a different approach. It acts as a transparent, distributed caching and augment on top of S3, combining the mountable experience of FSx, the ultra low latency of S3 Express, and the cost efficiency of standard S3 buckets, all without requiring data migration. You can keep your s3:// paths (or mount a POSIX path), point clients at the Alluxio endpoint, and run.
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.
Watch this on-demand video to learn about the latest release of Alluxio Enterprise AI. In this webinar, discover how Alluxio AI 3.7 eliminates cloud storage latency bottlenecks with breakthrough sub-millisecond performance, delivering up to 45× faster data access than S3 Standard without changing your code.
In the latest MLPerf Storage v2.0 benchmarks, Alluxio demonstrated how distributed caching accelerates I/O for AI training and checkpointing workloads, achieving up to 99.57% GPU utilization across multiple workloads that typically suffer from underutilized GPU resources caused by I/O bottlenecks.
In this talk, we’ll explore the engineering challenges of extending Apache Pinot—a real-time OLAP system—onto cloud object storage while still maintaining sub-second P99 latencies.
Built on the foundation of Parquet files, Iceberg adds a simple yet flexible metadata layer and integration with standard data catalogs to provide robust schema support and ACID transactions to the once ungoverned data lake. In this talk, we'll build Iceberg up from the basics, see how the read and write path work, and explore how it supports streaming data sources like Apache Kafka™.
In this talk, we show how architecture co-design, system-level optimizations, and workload-aware engineering can deliver over 1000× performance improvements for these workloads—without changing file formats, rewriting data paths, or provisioning expensive hardware.
Nilesh Agarwal, Co-founder & CTO at Inferless, shares insights on accelerating LLM inference in the cloud using Alluxio, tackling key bottlenecks like slow model weight loading from S3 and lengthy container startup time. Inferless uses Alluxio as a three-tier cache system that dramatically cuts model load time by 10x.
In this talk, Jingwen Ouyang, Senior Product Manager at Alluxio, will share how Alluxio make it easy to share and manage data from any storage to any compute engine in any environment with high performance and low cost for your model training, model inference, and model distribution workload.
In this webinar, David Zhu, Software Engineering Manager at Alluxio, will present the results of a joint collaboration between Alluxio and a leading SaaS and data infrastructure enterprise that explored leveraging Alluxio as a high-performance caching and acceleration layer atop AWS S3 for ultra-fast querying of Parquet files at PB scale.
Building a reliable, high-performance AI/ML infrastructure can be challenging, especially with constrained budget in a multi-GPU world: infrastructure teams have to leverage GPUs wherever they are available. This requires moving data across regions and clouds, which further leads to slow, complex, and expensive remote data access challenges. This white paper introduces the common causes of slow AI workloads and low GPU utilization, how to diagnose the root cause, and offer solutions to the most common root cause of underutilized GPUs.
This article introduces how to leverage Alluxio as a high-performance caching and acceleration layer atop hyperscale data lakes for queries on Parquet files. Without using specialized hardware, changing data formats or object addressing schemes, or migrating data from data lakes, Alluxio delivers sub-millisecond Time-to-First-Byte (TTFB) performance comparable to AWS S3 Express One Zone. Furthermore, Alluxio’s throughput scales linearly with cluster size; a modest 50-node deployment can achieve one million queries per second, surpassing the single-account throughput of S3 Express by 50× without latency degradation.
In a recent Alluxio-hosted virtual tech talk, Hyun Jun Baek, Staff Backend Engineer at Coupang, presented "How Coupang Leverages Distributed Cache to Accelerate ML Model Training." This blog post summarizes key insights from Hyun's presentation on Coupang's approach to distributed caching and how it has transformed their multi-region, hybrid cloud machine learning platform.