Videos

Unlock the full performance of your AI/ML infrastructure on Oracle Cloud Infrastructure (OCI).

Join Oracle's Master Principal Cloud Architect Xinghong He and Alluxio's VP of Technology Bin Fan for an in-depth technical session exploring how modern tiered caching, optimized storage integration, and smart deployment choices can deliver sub-millisecond latency and up to 5× faster data access on OCI — at scale.

You'll learn about:

• Architectural insights: How Alluxio’s tiered caching architecture works with OCI Object Storage and BM.DenseIO compute instances to eliminate data access bottlenecks.
• Benchmark-proven results: See real MLPerf Storage 2.0 and Warp benchmark outcomes demonstrating sub-millisecond latency and dramatic throughput gains.
• Deployment strategies: Compare deployment options — dedicated mode for peak performance vs. co-located mode for cost-efficient scale.
• Practical, actionable guidance: Implementation best practices you can apply directly to your AI/ML workloads on OCI.

Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.

With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.

In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.

They discuss:

• How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
• How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
• How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments

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In this talk, Eric Wang, Senior Staff Software Engineer introduces Uber’s open-source generative end-to-end ML lifecycle management platform: Michelangelo.