Adding intelligence at the edge
CIOs have been facing increasing pressure to deliver successful digital initiatives while managing budget constraints and rising demands from senior executives. A recent survey by Gartner indicates that 92% of CIOs plan to integrate artificial intelligence (AI) into their organizations by 2025, but nearly half of them struggle to evaluate and demonstrate the value of this technology. Are we stuck in a cycle here?
Amid these challenges, small language models (SLMs) have emerged as a viable solution, offering cost-effective and secure AI capabilities that align with strategic goals. The appeal of SLMs is evident in their ability to balance accuracy, speed, and cost-effectiveness, as highlighted by Amer Sheikh, Chief Data Scientist at BearingPoint.
Small language models like Mistral Small and DeepSeek R1 have gained popularity for their ability to provide practical alternatives to large language models (LLMs). These SLMs are driving the adoption of edge AI, enabling AI models to operate on smartphones, IoT devices, and industrial systems without relying on cloud infrastructure.
Despite being in the early stages, real-world applications of SLMs in mobile and IoT devices are promising. Examples include DeepSeek’s R1 model integrated into Chinese automakers’ infotainment systems and Phi-3 designed for mobile AI applications. SLMs are also being used in education, such as Stanford’s Smile Plug for interactive learning experiences on Raspberry Pi devices without internet connectivity.
Unlike LLMs that require significant computational power and cloud resources, SLMs can run locally, reducing costs and enhancing security. This makes them ideal for boosting edge device intelligence in industries like customer service, virtual assistants, and text summarization.
SLMs offer the advantage of focusing on specific industry use cases, making them popular in regulated sectors like telecoms, accounting, and law. Their deployment in professional services for accounting and telecom regulation, as well as on-device applications and home automation, showcases their versatility.
Security is a key focus for industries moving away from LLMs towards SLMs, especially in edge devices. SLMs offer transparency and on-premise data processing, reducing the risk of data breaches and ensuring compliance with regulations in sectors like healthcare and finance.
Lower energy consumption and reduced cloud dependency are driving the adoption of SLMs in edge devices. These models are cost-effective, environmentally friendly, and can run locally without internet access, enhancing data privacy and security.
The future of SLMs and edge computing devices holds promise for enterprises seeking AI differentiation and operational efficiency. As organizations prioritize AI models tailored to industry-specific needs, SLMs are poised to play a significant role in driving business performance and competitive advantage.
