ChatGPT and other massive models represents an amazing step forward in AI, yet they do not solve real-world business problems. In this session, Jordan Plawner, Global Director of Artificial Intelligence Product Manager and Strategy at Intel, surveys how the AI ecosystem has worked non-stop over this last year to take these all-purpose multi-task models and optimize them to they can be used by organizations to address domain specific problems. He explains these new AI-for-the-real world techniques and methods such as fine tuning and how they can be applied to deliver results which are highly performant with state-of-the-art accuracy while also being economical to build and deploy everywhere to enhance products and services.
ChatGPT and other massive models represents an amazing step forward in AI, yet they do not solve real-world business problems. In this session, Jordan Plawner, Global Director of Artificial Intelligence Product Manager and Strategy at Intel, surveys how the AI ecosystem has worked non-stop over this last year to take these all-purpose multi-task models and optimize them to they can be used by organizations to address domain specific problems. He explains these new AI-for-the-real world techniques and methods such as fine tuning and how they can be applied to deliver results which are highly performant with state-of-the-art accuracy while also being economical to build and deploy everywhere to enhance products and services.
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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
