The era of monolithic Large Language Models is ending. Discover why specialized, efficient Small Language Models (SLMs) are the key to scalable, economic, and high-performance AI Agents.
Agentic AI involves repetitive tasks: parsing tools, routing commands, and simple formatting. Using a massive 70B+ parameter model for these tasks is computationally wasteful.
The Thesis: SLMs (Small Language Models) offer a massive reduction in latency and cost while maintaining sufficient capability for ~80% of agentic sub-tasks.
NVIDIA Research analyzed popular agent frameworks to estimate what percentage of tasks could be reliably handled by SLMs. The results confirm that the majority of an agent's workload does not require GPT-4 level intelligence.
Percentage of LLM queries that can be handled by specialized SLMs in key frameworks.
Designed for General Computer Control via screenshots. Repetitive GUI interaction workflows and pre-learned click sequences are perfect for SLMs.
Aims to execute code locally. While complex coding needs an LLM, parsing execution results and formatting commands are trivial for SLMs.
Simple command routing and message generation based on templates can be completely offloaded to smaller models.
The paper proposes a system where a single powerful "General" model orchestrates a fleet of "Specialist" models. Click the components below to understand their roles.
The central orchestrator. This is a large model (e.g., GPT-4, Llama 3 70B). It handles high-level planning, complex reasoning, and "unstructured" error handling that requires deep context.
Monitor the existing LLM-based agent. Log all prompts and responses. Categorize inputs by their intent (e.g., "function call", "planning", "dialogue"). This establishes the baseline "workload" of the agent.