How can AI models scale without overwhelming computational resources? With AI systems becoming more advanced, practitioners must navigate the delicate balance between performance optimization and computational efficiency to ensure scalability and real-world applicability. This session explores quantization, pruning, and sparsity techniques to optimize model size without compromising accuracy, ensuring scalable and high-performing multimodal AI workloads. Also understand how to enhance real-time AI performance with adaptive workload scheduling, GPU/TPU acceleration, and serverless architectures while maintaining cost efficiency.