Maximizing AI Infrastructure Efficiency with Run AI's Orchestration Software

Table of Contents:

1. Introduction
2. The Challenges of AI Infrastructure in Financial Institutions
3. The Rise of AI Accelerators
4. The Growing Need for Computing Power in AI Development
5. Artificial Intelligence in Finance
6. The Challenges of Bringing AI Initiatives to Market
7. The Problem of Limited GPU Utilization
8. The Solution: Run AI's Orchestration Software
9. How Run AI Works: Dynamic Allocation and Guaranteed Quotas
10. Real-Life Case Study: Accelerating Time to Solution
11. Introducing Run AI's Fractional GPU Capability
12. Conclusion

The Challenges of AI Infrastructure in Financial Institutions

In the ever-evolving field of artificial intelligence (AI), financial institutions have become one of the major growth areas. The ability to predict risk, automate fraud detection, and optimize algorithmic trading has made AI an indispensable tool for these institutions. However, the road to implementing machine learning and deep learning initiatives is far from easy. IT leaders, MLOps teams, and data scientists often find themselves facing numerous challenges when it comes to managing their AI infrastructure.

The Rise of AI Accelerators

Over the past decade, the rise in readily accessible compute power has revolutionized the field of AI. Accelerators such as NVIDIA's GPUs have provided a powerful solution for running large numbers of highly compute-intensive training models in Parallel. However, building AI solutions requires a significant amount of computation, and as the amount of data and the size of AI models continues to grow rapidly, the need for computing power in data centers has scaled accordingly.

Artificial Intelligence in Finance

The financial industry has embraced the potential of AI, leveraging it for a wide range of use cases. From risk prediction to algorithmic trading, AI has proven to be a valuable tool for financial institutions. However, despite its potential, bringing AI initiatives to market in the finance sector is not without its challenges. Algorithm developers and data scientists often find themselves facing limitations in managing expensive compute resources, leading to suboptimal speed and utilization.

The Problem of Limited GPU Utilization

On average, customers utilizing run AI's orchestration software achieve only 25% utilization of their expensive GPU resources. This means that a significant amount of computing power is left untapped, leading to inefficiencies and wasted resources. The main reason behind this underutilization is the nature of data science, which revolves around running numerous experiments to build models. Unlike software development, data science requires variable amounts of resources at different times, making static resource allocations inefficient.

The Solution: Run AI's Orchestration Software

To address the challenges faced by financial institutions in managing their AI infrastructure, run AI offers a state-of-the-art orchestration software. This platform pulls together all available GPUs in the organization and dynamically allocates them across multiple users, teams, and applications based on preset priorities and policies. With run AI, users gain automated access to GPUs when they need them, without being limited by static resource allocations.

How Run AI Works: Dynamic Allocation and Guaranteed Quotas

Run AI's platform leverages a super scheduler tailored for orchestrating AI clusters. One key feature is the concept of guaranteed quotas, where each user is assigned a quota of GPUs. However, unlike traditional static allocations, run AI allows users to use more GPUs than their quota if available. When resources are scarce, the system intelligently reallocates GPUs from users who are under their quota to those who need them, ensuring fair access and maximizing resource utilization.

Real-Life Case Study: Accelerating Time to Solution

A real-life case study showcases the effectiveness of run AI's system in reducing the time taken to build AI solutions. By providing talented researchers with dynamic access to idle GPU resources, run AI enabled a researcher to reduce the time needed to build a model from 49 days to just two days, representing a remarkable 3000% improvement. The researcher was able to leverage the idle GPUs that would otherwise remain unutilized, driving innovation and accelerating time-to-market for financial solutions.

Introducing Run AI's Fractional GPU Capability

In addition to dynamic GPU allocation, run AI offers fractional GPU capability, where a single physical GPU can be fractioned into smaller virtual GPUs. This allows multiple deep learning workloads, such as Jupyter notebooks and inference jobs, to run concurrently on the same GPU without interference. Fractional GPUs provide cost reduction, improved utilization, and enable data scientists to run more models on existing hardware.

Conclusion

The challenges faced by financial institutions in managing their AI infrastructure are significant but can be overcome with the help of orchestration software like run AI. By dynamically allocating resources, maximizing utilization, and providing fractional GPU capabilities, run AI enables data scientists to be more productive and accelerates the deployment of AI solutions in the finance industry.

🚀 Run AI's orchestration software: www.run.ai