Jensen Huang on GPUs - Computerphile

This episode explores the rapid advancements in AI and their impact on GPU technology, featuring an interview with Jensen Huang. Against the backdrop of Moore's Law limitations, the conversation delves into the strategies Nvidia employs for scaling up and scaling out computing power. More significantly, Huang highlights the co-design approach, where software, algorithms, and chips are optimized simultaneously, enabling far greater acceleration than Moore's Law predicted. For instance, the shift from FP32 to lower precision formats like FP8 significantly increases computational power and reduces energy consumption. As the discussion pivoted to the future, Huang emphasizes the convergence of different GPU types, driven by the increasing importance of tensor cores in various applications, including computer graphics and AI. This convergence, coupled with the continuous evolution of AI models and algorithms, points to a future where AI plays a central role in optimizing and accelerating various computational tasks. What this means for the future of computing is a landscape where AI-driven optimization and hybrid approaches will be key to keeping pace with the relentless acceleration of computational demands.