Over the years, Large Language Models (LLMs) have dominated the scene. However, a notable shift is underway towards Small Language Models (SLMs), driven by concerns over privacy, latency, and cost that have hindered widespread adoption of Large Language Models among enterprises.
What’s Small Language Models?
The definition of SLMs is evolving alongside the exponential growth of LLMs in size and complexity. What once constituted a large model now often qualifies as small. Large Language Models carry billions to trillions of parameters, allowing them to manage complex tasks. On the other hand, Small Language Models like Microsoft Phi and Llama-7B operate with millions to a few billions of parameters offering enterprises flexibility to customize and control the models.
Advantages of Small Language Models
1. Small Language Models running on the edge provide unrivaled reliability and competitive advantage for real-time applications.
Small Language Models boast several advantages. Their compact size, easier maintenance, and integration into diverse software and web applications enable new use cases that were not possible previously. Unlike resource-intensive LLMs, SLMs balance efficiency and performance, making SLMs a great fit for real-time applications such as chatbots, voice assistants, and localized search engines, where on-device processing offers a competitive edge.
2. Small Language Models running on the edge offer intrinsic privacy, making them ideal for highly regulated industries and enterprise settings.
LLMs excel in versatility across tasks whereas SLMs are optimized for specific applications like customer service and coding, where personally identifiable and confidential information is exchanged. Their reduced computational demands cater to applications running on individuals’ resource-constrained machines, such as laptops, tablets, and mobile phones without sending user data to 3rd party cloud providers.
3. Small Language Models offer a cost-effective solution for the environment and high-volume applications.
Due to their smaller size, SLMs consume less energy and have a reduced carbon footprint when running applications, lowering operational costs. Given the fact that only a handful of companies can afford to lose millions of dollars every day by running or calling large language models running in the cloud. Running SLMs locally eliminates the infrastructure cost.
The Shift Towards Small Language Models
In summary, SLMs represent a paradigm shift by offering robust performance with optimized resource consumption. Leading industry giants like Apple, OpenAI and Google have made headlines with on-device AI and Small Language Models, such as Apple's on-device Apple Intelligence, OpenAI’s GPT-4o mini, and Google's Gemini Nano, indicating a growing trend toward widespread adoption.
Looking Ahead
Despite the bright future of SLMs, enterprises that cannot afford deep learning research teams had no options to leverage SLMs as it was not clear when Big Tech would roll them out fully, or allow other developers to leverage their Small Language Models running locally. Yet, enterprises do not need to wait for Big Tech. Picovoice’s picoLLM Local LLM platform allows enterprises to benefit from performant Small Language Models today. picoLLM Compression shrinks the size and large language models without sacrificing performance, making any large language model smaller. picoLLM GYM enables enterprises to train Small Language Models using their data.
In conclusion, the rise of Small Language Models marks a pivotal moment in AI development, democratizing access to sophisticated language processing capabilities while addressing practical concerns of scalability and efficiency. Picovoice Consulting works with enterprises that want not just to be a part of this pivotal moment but also lead it.
