The artificial intelligence landscape has been dominated by headlines about ever-larger language models—GPT-4 with its rumored trillion parameters, Claude with its massive context windows, and Google’s PaLM pushing the boundaries of scale. Yet a quieter revolution is happening in parallel: small language models (SLMs) with just 1-10 billion parameters are proving remarkably capable for specific … Read more
The explosion of large language models has captivated the world with their impressive capabilities, but their multi-billion parameter architectures and substantial computational requirements make them impractical for edge deployment. Edge devices—smartphones, IoT sensors, embedded systems, and industrial controllers—demand models that run efficiently on limited hardware while maintaining acceptable performance. Custom small language models, typically ranging … Read more
The artificial intelligence revolution has brought unprecedented capabilities to organizations of all sizes, but it has also introduced a significant challenge: cost. While large language models like GPT-4 and Claude have captured headlines with their impressive abilities, they come with substantial computational requirements and API costs that can quickly balloon into unsustainable figures for many … Read more
The AI landscape faces a fundamental tension: larger language models deliver better performance, yet their computational demands make deployment prohibitively expensive for many applications. Distillation—the process of transferring knowledge from large “teacher” models to smaller “student” models—has emerged as one of the most important techniques for resolving this tension. Understanding why distillation matters reveals not … Read more
The landscape of artificial intelligence deployment is undergoing a fascinating divergence. While Big Tech companies continue to push the boundaries with ever-larger language models, a quiet revolution is taking place in the startup world. Small language models—those with parameters ranging from hundreds of millions to a few billion—are becoming the weapon of choice for nimble … Read more
The rise of large language models has created a misconception that bigger always means better. While frontier models like GPT-4 and Claude capture headlines, small language models (typically under 7 billion parameters) offer compelling advantages for production systems: lower latency, reduced costs, enhanced privacy, and the ability to run on modest hardware. The challenge lies … Read more
The rapid advancement of natural language processing (NLP) has led to the development of various language models, ranging from large language models (LLMs) to small language models (SLMs). These models play a crucial role in powering applications like chatbots, summarization tools, translation systems, and more. However, the choice between a large or small model depends … Read more
