When training models on ultra-large datasets, one of the biggest challenges is low GPU utilization. These powerful processors are often underutilized due to inefficient I/O and data access. This mismatch between computation and storage leads to wasted GPU resources, low performance, and high cloud storage costs. The rise of generative AI and GPU scarcity is only making this problem worse.
In this webinar, Tarik and Beinan discuss strategies for transforming idle GPUs into optimal powerhouses. They will focus on cost-effective management of ultra-large datasets for AI and analytics.
What you will learn:
- The challenges of I/O stalls leading to low GPU utilization for model training
- High-performance, high-throughput data access (I/O) strategies
- The benefits of using an on-demand data access layer over your storage
- How Uber addresses managing ultra-large datasets using high-density storage and caching
When training models on ultra-large datasets, one of the biggest challenges is low GPU utilization. These powerful processors are often underutilized due to inefficient I/O and data access. This mismatch between computation and storage leads to wasted GPU resources, low performance, and high cloud storage costs. The rise of generative AI and GPU scarcity is only making this problem worse.
In this webinar, Tarik and Beinan discuss strategies for transforming idle GPUs into optimal powerhouses. They will focus on cost-effective management of ultra-large datasets for AI and analytics.
What you will learn:
- The challenges of I/O stalls leading to low GPU utilization for model training
- High-performance, high-throughput data access (I/O) strategies
- The benefits of using an on-demand data access layer over your storage
- How Uber addresses managing ultra-large datasets using high-density storage and caching
Video:
Presentation slides:
Videos:
Presentation Slides:
Complete the form below to access the full overview:
.png)
Videos
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
