Organizations are retooling their enterprise data infrastructure in the race for AI/ML. However, growing datasets, extensive data engineering overhead, high GPU costs, and expensive specialized storage can make it difficult to get fast results from model development.
The data access layer is the key to accelerating your path to AI/ML. In this webinar, Roland Theron, Senior Solutions Engineer at Alluxio, discusses how the data access layer can help you:
- Build AI architecture on your existing data lake without the need for specialized hardware.
- Streamline the time-consuming process of managing data copies in data engineering.
- Speed up training workloads with high GPU utilization.
- Achieve optimal concurrency to deliver models to inference clusters for demanding applications
Organizations are retooling their enterprise data infrastructure in the race for AI/ML. However, growing datasets, extensive data engineering overhead, high GPU costs, and expensive specialized storage can make it difficult to get fast results from model development.
The data access layer is the key to accelerating your path to AI/ML. In this webinar, Roland Theron, Senior Solutions Engineer at Alluxio, discusses how the data access layer can help you:
- Build AI architecture on your existing data lake without the need for specialized hardware.
- Streamline the time-consuming process of managing data copies in data engineering.
- Speed up training workloads with high GPU utilization.
- Achieve optimal concurrency to deliver models to inference clusters for demanding applications
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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
