Small Language Models are the Future of Agentic AI?
Nvidia published a research paper about Small Language Models in Agentic AI: https://arxiv.org/abs/2506.02153
The paper argues that the future of effective AI agents lies not with massive, general-purpose Large Language Models (LLMs), but with smaller, more specialized Small Language Models (SLMs).
Key Findings & Arguments
SLMs Are Powerful Enough: Models like Microsoft's Phi-3 and NVIDIA's Nemotron-H family can match the performance of much larger models on specific agentic tasks like tool calling, code generation, and instruction following.
SLMs Are More Economical & Efficient: They are 10–30 times cheaper to run, have lower latency, require less energy, and can be fine-tuned overnight instead of over weeks. This also allows them to run on consumer devices (edge deployment), enhancing speed and data privacy.
Agents Don't Need Generalists: Most AI agents restrict a powerful generalist LLM to perform a very narrow set of functions. The paper argues it's more logical to use an SLM fine-tuned specifically for that narrow function. This also ensures better behavioral alignment, as an SLM can be trained to produce outputs in a strict format (like JSON) that the agent's code expects, reducing errors.
The Future is Heterogeneous: The ideal agentic system is heterogeneous or "Lego-like." It should use a collection of specialized SLMs for common, simple tasks and only call upon a powerful, expensive LLM for complex reasoning or general conversation when absolutely necessary.
What I think:
While SLMs will certainly be useful for specific products and use cases, I think general-purpose LLMs will remain the primary choice for most companies. This is because many organizations lack the specialized engineering know-how to implement SLMs effectively.
Successfully implementing SLMs requires a high level of proficiency in data collection, cleaning, and obfuscation for fine-tuning. It also demands a solid, foundational understanding of LLMs and AI in general.
Considering that most companies struggle with even the basic aspects of building AI agents, I think that SLMs will, at least for now, be reserved for specialized workflows handled by more experienced AI developers.