Empowering Seamless Driving: KATYUS MotusAI Accelerates Smart Driving Innovation for Leading South Korean Automaker

A Case Study on AI Infrastructure Transformation for Intelligent Driving with KAYTUS MotusAI

As autonomous driving technology progresses toward large-scale deployment, leading automotive manufacturers must overcome significant data, compute, and operational challenges. A major South Korean automotive company partnered with KAYTUS to modernize its AI infrastructure and scale its L4 autonomous driving capabilities. By deploying the KAYTUS MotusAI platform, the company achieved substantial improvements in training efficiency, compute utilization, and overall AI development agility.

Customer Introduction

Our client is a premier automotive manufacturer in South Korea, recognized for its leadership in autonomous driving and advanced driver-assistance systems (ADAS). The company boasts full-stack capabilities—from core algorithm design and system architecture to vehicle integration and mass production. With a strong commitment to intelligent mobility, the client is pioneering the transition from experimental prototypes to commercially viable autonomous vehicles.

Project Background

To accelerate the mass production of its L4 autonomous driving system, the client faced growing challenges: increasingly complex deep learning models, massive data volumes, and rapidly evolving development cycles. These models are critical for tasks like obstacle detection, lane recognition, path planning, and behavior prediction—each demanding enormous computing power. To meet these needs, the client aimed to build a high-performance, centrally managed AI platform that integrates compute, data, and development environments. The goal was to streamline the AI development lifecycle and significantly boost training efficiency and resource utilization.

Business Challenges

Transition to mass production of L4-level autonomous systems presented several challenges:

> Low Compute Efficiency with Concurrent Tasks:

• Underutilized or overburdened GPUs

• Scheduling inefficiencies from legacy resource managers

> Complex Environments Hindering Training and Productivity:

• Distributed training setups were time-consuming and error prone

• I/O bottlenecks slowed the training process 

> Fragmented AI Development and Operations: 

• Integrating heterogeneous compute, data, and storage

• Managing deployments across disjointed environments

• Delays in initiating and scaling new projects

KAYTUS Solution

To address these challenges, KAYTUS implemented MotusAI, a software-hardware fully integrated, optimized AI platform tailored for high-performance, centralized, and scalable AI development.

Key Solution Features:

> End-to-End AI Lifecycle Management

• Unified control of compute, storage, and development environments

• Granular and smart scheduling and orchestration for heterogeneous resources

• High-speed local caching and streamlined data access

> Automated and Agile AI Development

• Concurrent training capacity of 4–5 tasks with up to 66% shorter training cycles

• Minute-level task scheduling enabling rapid experimentation and iteration

• One-click deployment for distributed training workloads

> Optimized Platform Operations

• Centralized platform management significantly reduces manual intervention

• Robust monitoring and logging for enhanced reliability and visibility

Outcomes & Business Impact

Following the deployment of KAYTUS MotusAI, the client realized measurable improvements:

• Resource Utilization increased from 70% to over 90%

• Training Efficiency improved by 35%, accelerating time-to-market

• Operational Overhead was significantly reduced

• Energy and Cost Savings from enhanced infrastructure efficiency