Small Language Models (SLMs) are revolutionizing the way artificial intelligence is deployed and consumed. According to Data Conversationalist<\/a>, SLMs are faster, cheaper, and more efficient than giant Large Language Models (LLMs). They run on laptops, phones, Raspberry Pi, or edge devices, making them ideal for privacy-sensitive industries.<\/p>\n SLMs have several benefits, including low computational requirements, privacy-preserving local processing, and fast inference speed. As stated in Medium<\/a>, SLMs are suitable for resource-constrained environments and easier to run on personal or edge devices.<\/p>\n SLMs have various real-world applications, including offline assistants, workflow automation, healthcare, IoT, and customer support. For instance, LinkedIn<\/a> highlights the use of TinyML (Tiny Machine Learning) in bringing AI capabilities to highly resource-constrained microcontroller units (MCUs) and embedded devices.<\/p>\n\n
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