Amid the dynamic realm of artificial intelligence (AI), a noteworthy industry pattern involves embracing smaller, specialized AI models. This trend aligns with the transformation observed in the hardware domain, with graphics processing units (GPUs), tensor processing units (TPUs), and other accelerators playing a crucial role in enabling efficient computing.
The fundamental driver behind this transition is the inherent tradeoff in central processing units (CPUs). CPUs, designed as versatile computing engines, execute a wide range of tasks, leading to increased silicon, energy, and time requirements. As explained by experts, this generality, while offering versatility, compromises efficiency.
Specialized AI hardware, including GPUs, TPUs, and NPUs, addresses this efficiency challenge. These dedicated engines, although less generalized than CPUs, excel in specific tasks by allocating more transistors and energy directly to computing and data access for the given task. This efficiency stems from their simplicity and economy, enabling parallel processing and increased operations per unit of time and energy.
A parallel evolution is unfolding in the domain of large language models (LLMs). While general models like GPT-4 showcase impressive generality, the associated costs in terms of parameters, compute, and memory access have prompted the emergence of specialized models. Examples like CodeLlama, Llama-2-7B, Mistral, and Zephyr demonstrate the trend towards smaller, more efficient models tailored for specific tasks.
In the realm of LLMs, the future lies in deploying a multitude of simpler models for the majority of AI tasks. The goal is to reserve larger, resource-intensive models for tasks that genuinely necessitate their capabilities. This approach, driven by the fundamental principles of physics, emphasizes that simpler operations demand fewer electrons, translating to greater energy efficiency.
The future of AI is not centered on building ever-larger general models but on embracing the power of specialization. The industry's focus on smaller, task-specific models reflects a commitment to sustainable, scalable, and efficient AI solutions. As experts highlight, this shift is not just a technological choice but an imperative dictated by the laws of physics.
