KAYTUS MotusAI Empowers Middle East Bank’s AI Center, Delivering 7x Increase in Model Training Speed

A Case Study on High-Performance AI-driven Financial Services with KAYTUS MotusAI

As digital banking evolves, financial institutions must deliver AI-driven services with speed, efficiency, and scalability. A leading commercial bank in the Middle East partnered with KAYTUS to establish a high-performance intelligent computing center. With the KAYTUS MotusAI platform, the bank achieved transformative gains in model training speed, infrastructure efficiency, and seamless integration with core banking systems.

Customer Overview

A major commercial bank in the Middle East is spearheading digital transformation by building an AI-powered computing center. Designed to handle real-time inference, high-volume model training, data storage and integrated analytics, this center supports rapid innovation and superior customer experiences across banking services.

Project Background

As business volume grew, the bank’s GPU infrastructure expanded to support nearly 1,000 online inference scenarios. However, challenges with inflexible architecture, inefficient resource management, and limited DevOps capabilities hampered its ability to scale AI training. Coordination among compute, networking, and storage resources emerged as a critical performance bottleneck.

Business Challenges

Inefficient Environment Setup & Idle Resources

• Overloaded image repositories slow distributed training and leave GPUs idle

• Network and local I/O latency bottleneck reduce training throughput

Complexity in Managing Heterogeneous Hardware

• Difficult to manage diverse accelerator types

• Lack of real-time monitoring reduces resource visibility

Network & Scheduling Difficulties

• RoCE-based RDMA networking demands low-latency lossless transmission and cross-   node GPU sharing

• No streamlined method for exposing physical RoCE NICs to containers

Tight Integration with Business Systems

• Multiple internal systems manage data, models, and inference separately

• Lack of unified, secure APIs slows deployment

KAYTUS Solution

To address these challenges, KAYTUS deployed the MotusAI platform—an integrated, AI-optimized solution tailored for enterprise-scale financial services.

End-to-End Job Management

• Real-time job tracking, usage analytics and optimization suggestions

• Full-stack access for configuration, quota, and scheduling control of heterogeneous resources

• Centralized health monitoring with developer-friendly visibility

Streamlined Heterogeneous Compute Management

• Configuration access and quota settings by hardware type

• Live monitoring and alerts for hardware status and usage

Accelerated Image Distribution & Data Caching

• P2P image sharing reduces setup time by 50%

• Local caching reduces latency; enables lifecycle-aware scheduling

Robust Fault Detection & Recovery

• Auto-recovery for GPU/network failures

• Higher reliable utilization with less manual intervention

Advanced RoCE Virtualization & Interoperability

• SR-IOV physical RoCEv2 NICs’ split to virtual NICs allowing elastic GPU scheduling

• Three-tier (switch-host-container) flow control ensures high bandwidth and low latency

• Custom interoperability checks maintain stable RDMA communication across nodes

• ResNet50 training accelerated 94% with no inter-node loss

Enterprise-Ready Platform Integration

• Secure APIs for managing tasks, data, and systems

• Fully encrypted communications with existing banking platforms

Outcomes & Business Impact

• Faster Time-to-Value: Training time cut from 1 week to 1 day

• Improved Efficiency: P2P caching and fault recovery cut idle time; Higher density and reduced idle resources

• Lower Ops Costs: 30% reduction in maintenance via modular platform

• Reliable Networking: Stable, cross-node GPU communication with RoCE

• Scalable Growth: Unified APIs support future banking digital transformation and innovations