Use Cases

Real-world applications of Rapt’s GPU automation technology.

USE CASE | BIOTECH

Accelerating Value And Build Model Capacity

The implementation of Rapt’s solution has delivered substantial outcomes: model training accelerated by 3.4x through optimized parallelism, setup times reduced by up to 90%, leading to enhanced productivity for data scientists. GPU utilization was maximized with intelligent virtualization and scheduling, ensuring high ROI without resource waste.. Scalability was achieved by tripling model build capacity and expanding AI compute capabilities across diverse environments.

challenges

  • Time-consuming trial-and-error setup and dynamic model infra requirements, OOM errors, and guesswork plagued model training.

  • Static GPU allocations led to over-provisioning and underutilization.

  • Cloud and on-premises silos caused scare and PU resources.

  • Challenges in managing data privacy and data gravity.

  • One-size-fits-all model configurations lacked flexibility.

SOLUTIONS

  • The CRE™ automates model runs, sizes infrastructure, optimizes GPU allocation, and predicts optimal GPU requirements, cutting setup times by up to 90%. This boosts data scientist productivity with no delays or errors.

  • Rapt determines model optimizations and distribution strategies based on available GPUs and communication latencies, reducing training times by 75% and eliminating manual parameter adjustments.

  • Rapt intelligently shares and fractionalizes GPUs based on model requirements, maximizing utilization without user intervention. This speeds up model experimentation and delivery.

  • The platform securely accesses GPUs across any cloud for optimal cost and performance, ensuring data remains local and private. Hybrid/multi-cloud deployment ensures increase GPU supply, model scalability and cloud vendor independence. 

  • Rapt allows setting SLA priorities for performance, cost, latencies, and availability. It optimizes GPU resources to maximize ROI without overprovisioning, ensuring efficient resource allocation for each model.

The Outcome

A multinational Fortune 100 pharmaceutical company faced a critical need to get data science results faster without increasing their spend on infrastructure. They developed an AI platform for hundreds of data scientists specializing in LLM AI models for drug discovery models. The company's AI infrastructure included public cloud GPU instances, on-premises NVIDIA DGX servers, and over 300 data scientists spread across multiple continents.

Other Impressive Stats

70%

Reduction in build model times

90%

Reduction in set-up times

4X

Faster training and inference model run times

secure

Multi-cloud deployment achieving vendor independence

Transforming GPU Utilization And Client Support

USE CASE | ADAS MSP

68%

COST SAVINGS

4X

MORE USERS SUPPORTED

98%

GPU UTILIZATION

This national ADAS MSP needed to provision and manage low-cost GPU clusters for their Al research and servicing their numerous clients. Unfortunately, the company encountered several issues, including…

  • High GPU costs and non-shareable resources with a 1:1 allocation ratio. These issues led to underutilized GPUs and poor resource planning.

  • After using the Rapt platform, the ADAS MSP could efficiently manage their GPU resources, significantly reduced costs, and support a greater number of

The implementation of Rapt’s solution has delivered substantial outcomes: model training accelerated by 3.4x through optimized parallelism, setup times reduced by up to 90%, leading to enhanced productivity for data scientists. GPU utilization was maximized with intelligent virtualization and scheduling, ensuring high ROI without resource waste. Scalability was achieved by tripling model build capacity and expanding AI compute capabilities across diverse environments.

USE CASE | MEDTECH

Elevating AI Efficiency With Next-Gen GPU Resource Management

3.5X

Increase in images generated

76%

Cost Savings

89%

GPU Utilization

This MedTech company required a solution for diagnosing diseases using AI, specifically employing GANs to generate synthetic images for disease detection. They needed to run these GANs in a private cloud environment.

  • The company faced several challenges. The image generation process was slow, and high GPU costs were a significant concern. They were able to generate fewer images than needed due to underutilized GPUs, which typically operated at only 32% capacity. Additionally, the existing setup enforced a 1:1 generator-to-GPU ratio, which prevented multiple GANs from running in parallel on a single GPU.

  • By leveraging Rapt's platform, the MedTech company could efficiently utilize their GPUs, reduce costs, and significantly increase their image generation output by an outstanding 3.5x!

TESTIMONIAL

"The Rapt platform allows our Data Scientists to run Al models with one-click. This eliminates infra setups and resource configurations, increasing productivity by at least 4x. They can also run 3x more models in the same infrastructure and we pay 70% less to cloud while maximizing our on-premise Al servers."

- Global Life Sciences | Sr. Manager, Al Platforms

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