Supercharge Your AI Development with Run AI

Supercharge Your AI Development with Run AI

Table of Contents

1. Introduction
2. Challenges with AI Infrastructure in Financial Institutions
   • Underutilized compute resources
   • Static GPU allocations
3. The Rise of AI Accelerators
4. The Growing Need for Computing Power in AI Solutions
5. The Importance of AI in the Finance Industry
   • Risk prediction
   • Fraud detection
   • Algorithmic trading
6. The Challenges in Bringing AI Initiatives to Market
   • Limited ability to manage compute resources
   • Low utilization and suboptimal speed
7. Introducing Run AI: An Orchestration Software Platform
   • Dynamic and elastic cloud-native platform
   • Centralized and high-performance cluster orchestration
   • Guaranteed quotas to optimize resource utilization
8. Case Study: Improving Time-to-Solution with Run AI
   • Real-life example of reducing solution building time from 49 days to 2 days
   • Dynamic allocation of GPUs for concurrent workloads
9. Fractional GPU Capability for Enhanced Resource Utilization
   • Creating multiple smaller virtual GPUs from a single physical GPU
   • Cost reduction and improved utilization for build models and inference workloads
10. Conclusion

Challenges with AI Infrastructure in Financial Institutions

Artificial intelligence (AI) has seen significant growth in the finance industry, with applications such as risk prediction, fraud detection, and algorithmic trading. However, financial institutions face various challenges in their AI initiatives. One major challenge is the underutilization of compute resources. On average, customers utilizing AI resources achieve only 25% utilization, resulting in wasted resources and increased costs.

Another challenge is the static allocation of GPUs. AI development relies heavily on running compute-intensive training models in Parallel, which require specialized and expensive GPUs. However, data science teams often have limited ability to manage these resources effectively, leading to suboptimal speed and utilization.

The Rise of AI Accelerators

In the past decade, there has been a significant rise in the availability of AI accelerators, such as NVIDIA's GPUs. These accelerators provide readily accessible compute power, enabling faster AI development and training. However, with the increasing amount of data and the growth of AI models, the demand for computing power in data centers has also scaled rapidly.

The Importance of AI in the Finance Industry

The finance industry has been one of the biggest growth areas for AI. This is not surprising, considering the wide range of use cases that AI can address in financial institutions. Predicting risk, detecting fraud, and enabling algorithmic trading are just a few examples of how AI can revolutionize financial services. However, bringing AI initiatives to market in the finance industry is not without its challenges.

The Challenges in Bringing AI Initiatives to Market

Developing and deploying machine or deep learning initiatives in the finance industry is not an easy task. IT leaders, MLOps teams, and data science teams often struggle with limited ability to manage expensive compute resources efficiently. As a result, the speed and utilization of AI experimentation suffer. Data scientists typically run a variable number of experiments at different times to build models, and the static allocation of resources hampers their productivity.

Introducing Run AI: An Orchestration Software Platform

To address the challenges faced by financial institutions in their AI initiatives, Run AI offers a state-of-the-art orchestration software platform. The platform aims to take organizations from the dark age of AI infrastructure, where manual engineering and static resource allocation are the norm, to a dynamic and elastic cloud-native platform.

With Run AI, every user can access any number of GPUs when they need them, without being limited by static resource allocations. The platform pulls together all available GPUs in the organization and dynamically allocates them across multiple users, teams, and applications Based on presets, priorities, and policies. This centralized and high-performance cluster orchestration ensures optimal utilization of compute resources.

One key feature of Run AI is the concept of guaranteed quotas. Instead of being limited by their quotas, users can utilize more GPUs than allocated if available. When users under their quota ask for a GPU, the system automatically reclaims GPUs from users who are over their quota, based on organizational policies and priorities. This seamless resource allocation process improves fairness and maximizes GPU utilization.

Case Study: Improving Time-to-Solution with Run AI

A real-life case study exemplifies the impact of Run AI in the financial industry. One customer was able to reduce the time taken to build a solution from 49 days to just two days, representing a significant 3000% improvement in speed. By leveraging idle GPU resources and the dynamic allocation capabilities of Run AI, a researcher was able to accelerate their experimentation and bring solutions to market faster.

Through the simultaneous execution of 50 concurrent workloads, the Run AI system managed the allocation of GPUs, allowing users to run more experiments and achieve better science. With the ability to submit as many jobs and experiments as needed, data scientists experienced increased productivity and reduced time-to-market.

Fractional GPU Capability for Enhanced Resource Utilization

Run AI offers an innovative feature called fractional GPU capability, which allows the creation of multiple smaller virtual GPUs from a single physical GPU. This capability virtualizes logical GPUs, each with its own memory and computing space. Multiple deep learning workloads, such as Jupyter notebooks or inference jobs, can run simultaneously on the same GPU without interfering with each other.

Fractional GPUs provide significant cost reduction and improved utilization of GPU resources. Data scientists can run more models on existing hardware, accelerating research and development. This feature enables data science teams to maximize their productivity and bring solutions to market more efficiently.

Conclusion

In the fast-paced world of AI, financial institutions face unique challenges in optimizing their infrastructure for optimal utilization and speed. Run AI's orchestration software platform offers a solution that addresses these challenges. By providing dynamic GPU allocation, guaranteed quotas, and fractional GPU capabilities, Run AI enables organizations to maximize resource utilization and accelerate time-to-solution. With Run AI, every data scientist has access to a supercomputer, unlocking new possibilities for innovation in the finance industry.