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How one company went from 28% GPU utilization to 73% with Run:ai
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- Software
- Telecommunications
Applicable Functions
- Business Operation
- Product Research & Development
Use Cases
- Computer Vision
- Predictive Maintenance
Services
- Data Science Services
- System Integration
The Challenge
The company, a world leader in facial recognition technologies, was facing several challenges with their GPU utilization. They were unable to successfully share resources across teams and projects due to static allocation of GPU resources, which led to bottlenecks and inaccessible infrastructure. The lack of visibility and management of available resources was slowing down their jobs. Despite the low utilization of existing hardware, visibility issues and bottlenecks made it seem like additional hardware was necessary, leading to increased costs. The company was considering an additional GPU investment with a planned hardware purchase cost of over $1 million dollars.
About The Customer
The customer is a multinational company that is a world leader in facial recognition technologies. They provide AI services to many large enterprises, often in real-time. Accuracy, measured in terms of maximizing performance of camera resolution and FPS, density of faces, and field of view are critically important to the company and their customers. They have an on-premises environment with 24 Nvidia DGX servers and additional GPU workstations, and a team of 30 researchers spread across two continents.
The Solution
The company implemented Run:ai's platform to address their challenges. The platform increased GPU utilization by moving teams from static, manual GPU allocations to pooled, dynamic resource sharing across the organization. It also increased productivity for the data science teams using hardware abstraction, simplified workflows, and automated GPU resource allocations. The platform provided visibility into the GPU cluster, its utilization, usage patterns, wait times, etc., allowing the company to better plan hardware spending. Furthermore, it accelerated training times, using automated, dynamic allocation of resources which enabled the data science teams to complete training processes significantly faster.
Operational Impact
Quantitative Benefit
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