AI-Powered High-Performance Computing for Real-Time Data Processing in 6G Networks

Artificial intelligence drives a paradigm for high-performance computing (HPC) to enhance the computational resource management of 6G networks. Deep reinforcement learning, dynamic job scheduling, and predictive traffic load forecasting improve real-time flexibility, ultra-low latency (1.5 milliseconds), and energy efficiency (0.010 joules per bit). Intelligent task prioritization and adaptive load balancing dynamically allocate computer resources. This approach provides uninterrupted AI-powered applications like autonomous systems, telemedicine, and smart city infrastructures. This framework outperforms existing optimization approaches in speed (80 Gbps), security (99%), and scalability (1600 nodes). Furthermore, it gains all these benefits simultaneously. This multi-agent reinforcement learning approach optimizes bandwidth consumption by 90% and reduces network congestion by 85%, improving job scheduling. Real-time monitoring, feedback-driven optimization, and machine learning-based decision-making ensure continuous system improvement. This method boosts reliability and efficiency. The system has a 97% fault tolerance, ensuring reliable performance even under changing network circumstances. It detects congestion patterns and reallocates resources to ensure optimum performance in changing situations using predictive modeling. For edge computing units and Internet of Things sensors with minimal resources, its 18% processing overhead is ideal. This study sets a new bar for AI-powered 6G network computational efficiency, enabling intelligent, self-optimizing, scalable high-performance computing systems. This study indicates that AI-driven techniques enable fast, safe, and energy-efficient digital ecosystems in next-generation networks.