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The industry is shifting towards deploying smaller, more specialized AI models, similar to the transformation we saw with hardware. In the hardware world, we moved from general CPUs to more efficient graphics processing units (GPUs), tensor processing units (TPUs), and other hardware accelerators. This change is driven by basic physics.
The CPU Tradeoff
CPUs are built as general computing engines capable of handling a wide range of tasks, from data sorting to calculations and device control. However, this versatility comes at a cost. Supporting a broad range of tasks demands more silicon, energy, and time, which makes CPUs less efficient.
Specialized Computing
Over the past 10-15 years, specialized computing has become more common. Today, AI discussions often mention GPUs, TPUs, and NPUs. These specialized engines perform fewer tasks than CPUs but are much more efficient because they focus more on computing and data access for specific tasks. Their simplicity allows systems to have many of these engines working in parallel, increasing efficiency.
Large Language Models
A similar change is happening with large language models (LLMs). General models like GPT-4 can perform many complex tasks but at a high cost in terms of parameters and the required computing power. Specialized models, such as CodeLlama for coding tasks or Llama-2-7B for language tasks, are more accurate and cost-effective. Smaller models like Mistral and Zephyr are also performing specific tasks efficiently.
This trend mirrors the shift from relying solely on CPUs to using GPUs for tasks requiring parallel processing, such as AI and simulations.
Efficiency Through Simplicity
The future of LLMs involves deploying simpler models for most AI tasks and reserving larger models for tasks that truly need their capabilities. Many enterprise applications, such as data manipulation and text classification, can be effectively handled by smaller, specialized models. The principle is simple: fewer operations mean less energy use, leading to greater efficiency. This doesn’t just make technological sense; it’s necessary due to the principles of physics. The future of AI lies in specialization for scalable and efficient solutions.
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