Optimizing AI Workloads for Maximum Performance

AXELTEC SOFTWARE LTD specializes in modern AI workload optimizations, including LLMs and other transformer-based libraries, built by engineers with years of experience at Intel and contribution to Apache TVM.

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About AXELTEC SOFTWARE LTD

Expert in Modern AI Workload Optimizations

AXELTEC SOFTWARE LTD is a specialized technology company focused on optimizing AI workloads for maximum performance and efficiency. Based in Cyprus, our team consists of experienced engineers who have contributed to major projects like OpenVINO at Intel and Apache TVM.

We understand the complex challenges of modern AI infrastructure and provide tailored solutions to help organizations maximize their computational resources while minimizing costs.

Our expertise spans across various AI frameworks and hardware accelerators, allowing us to deliver optimized solutions for any computational environment.

Our Services

LLM Optimization

We optimize Large Language Models for speed, efficiency, and reduced memory footprint, making deployment on various hardware configurations more accessible and cost-effective.

Computer Vision Expertise

Leveraging our deep experience with OpenVINO and optimization techniques, we provide high-performance computer vision solutions for edge devices, retail analytics, and industrial applications.

Custom AI Infrastructure

Design and implementation of tailored AI infrastructure solutions that match your specific workload requirements, ensuring optimal performance and cost efficiency.

Our Expert Team

Our team consists of engineers with years of experience at Intel, where they contributed to the development of OpenVINO, and have made significant contributions to the Apache TVM project.

Engineering Leadership

Former Intel engineers who built OpenVINO, bringing deep expertise in hardware optimization and compiler technology for AI workloads.

Research & Development

Contributors to Apache TVM with extensive experience in compiler optimization and hardware acceleration for machine learning workloads.

Contact Us

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