In “Small Language Models (SLMs) Are the Future of Agentic AI — Here’s Why,” Sambo Chea highlights a shift in how AI agents are being designed. Drawing from a comprehensive whitepaper by NVIDIA Research, the article argues that models with fewer than 10 billion parameters are not just adequate, but often preferable for many agentic applications—systems that act autonomously, interact with tools or APIs, and automate tasks. Rather than defaulting to gigantic models, developers are encouraged to embrace leaner, task-focused SLMs that deliver efficiency without compromising capabilities.
Relation to Neon AI:
This concept aligns closely with Neon AI’s vision for agentic, voice-enabled assistants. Neon AI’s BrainForge process enables efficient custom fine-tuning of SLMs, making it viable for even individuals or small businesses to build tailored, high-performing agents without requiring large infrastructure. When paired with the article’s core argument—that smaller, specialized models are more suited to agentic AI—this positions Neon’s approach as both practical and philosophically aligned with the future of autonomous, adaptable AI systems.
Read more at this link.