Tech Talk: How Fireworks AI Achieves 1TB/s+ Throughput for Model Deployment Across Multi-Cloud GPU Infrastructure
Please register here: https://www.alluxio.io/events/tech-talk-how-fireworks-ai-achieves-1tb-s-throughput-for-model-deployment-across-multi-cloud-gpu-infrastructure
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 with out requiring dedicated infrastructure resources.
In this tech talk, an Engineering Manager at Fireworks AI and Bin Fan, VP of Technology at Alluxio, will share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They will 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
Speakers:
Engineering Manager @ Fireworks AI
Bin Fan, VP of Technology @ Alluxio
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