“The whole is greater than the sum of its parts” – One giant generalist LLM (Large Language Model) vs swarms of SLM (Small Language Model)
Because of their smaller size and lower pre-training and fine-tuning costs, SLMs are naturally more flexible than LLMs in agentic systems. This makes it far more affordable and practical to train, customize, and deploy multiple specialized models tailored to different agentic tasks. This approach also enables democratization of AI development, allowing broader participation and faster innovation from niche players in Agentic AI space.
Thing to watch for – will the efficiency and flexibility from a collection of SLMs outweigh the overhead of managing them ?
I’m genuinely excited about the potential of SLMs in agentic AI. Unlike large models, which are often slow and resource-heavy to adapt, SLMs allow us to distill LLM capabilities into smaller, domain-specific versions quickly and efficiently. This means organizations can build specialized agents faster, fine-tune them at lower cost, and deploy them in real-world workflows without the heavy infrastructure demands of LLMs. The result is a more agile and scalable approach to agentic AI, where specialized SLMs can collaborate like expert teams, each optimized for a precise role.
