In our department, there is a 40-person Artificial Intelligence Laboratory designed to support research and development activities in the fields of artificial intelligence, machine learning, and big data. This laboratory is equipped with a specialized hardware infrastructure for training and testing high-computational models locally.

Thanks to its powerful computers equipped with NVIDIA RTX 40 series graphics cards, the laboratory enables high-speed execution of the training and fine-tuning processes of large language models (LLM) and deep learning architectures. The high parallel processing capacity of RTX 40 series GPUs provides a significant advantage especially for transformer-based models, image processing networks, and generative artificial intelligence (GenAI) applications.

Each system includes 64 GB of RAM, which makes it possible to process large datasets in memory, train complex model architectures, and run multi-process experiments without interruption. In this way, researchers are able to manage their data securely by working entirely in a local environment without depending on cloud infrastructure and can implement high-performance artificial intelligence projects in a short time.

The laboratory infrastructure is supported by a network-connected system architecture, high-speed data transfer infrastructure, and modern visualization systems, offering an advanced project development environment for undergraduate, graduate, and doctoral students.

Studies Conducted in the Laboratory:

• Local training and fine-tuning of large language models (LLM)
• Image, audio, and text-based deep learning projects
• GPU-accelerated machine learning experiments
• Generative AI, data augmentation, and model optimization applications
• Cloud-independent, secure data analysis and model deployment scenarios

The Artificial Intelligence Laboratory provides a modern R&D environment that not only allows our students to develop artificial intelligence applications but also enables them to improve their skills in efficiently using computational resources, optimizing model performance, and solving real-world problems.