Small Language Models (SLMs) are gaining momentum as an efficient alternative to Large Language Models (LLMs) in enterprise AI solutions. With lower computational requirements and faster training times, SLMs appeal to companies that may lack the resources to invest in the extensive datasets and financial outlay associated with LLMs. The demand for SLMs is projected to grow steadily over the next five years as they are tailored for specific tasks and offer adaptability through open-source platforms. SLMs are particularly effective in time-sensitive applications, aiding enterprises in forecasting and decision-making processes.
In the world of AI, small is becoming very big. Small Language Models (SLMs) are rapidly gaining traction in enterprise settings due to their lower resource requirements.
Compared to Large Language Models (LLMs), SLMs can be faster to train and deploy, making them a favorable option for companies with limited resources or urgent needs.
SLMs are not only growing in popularity, but they also allow companies to avoid the financial burdens associated with training LLMs, which can run into millions.
The adaptability of SLMs allows them to be built from scratch or modified from existing LLMs, positioning them as a versatile option for enterprises.
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