How Small Language Models Are Key to Scalable Agentic AI

TL;DR

• Small language models (SLMs) can handle core, repetitive agentic tasks with lower cost, lower memory, and faster inference than large language models (LLMs).
• A heterogenous architecture—SLMs for routine subtasks and LLMs for select open-ended tasks—offers flexibility and efficiency for real-world agentic AI.
• NVIDIA’s Nemotron Nano 2 (a 9B parameter SLM) demonstrates strong performance with 128k token contexts and 6x higher throughput, while keeping open weights and enterprise-ready tooling.
• The transition to SLM-enabled agents can be incremental: collect usage data, cluster tasks, fine-tune with LoRA/QLoRA, and modularize subtasks over time.
• NVIDIA NeMo provides end-to-end tooling to curate data, customize models, safeguard responses, and monitor agentic AI systems.

Context and background

Agentic AI is reshaping automation and productivity across enterprises by enabling AI agents to perform core operational tasks. These agents typically rely on large language models (LLMs) for general reasoning and dialogue, but LLMs are not always the most efficient or economical choice for every subtask within an agent workflow. A recent NVIDIA position paper argues for integrating small language models (SLMs) into agentic architectures to reduce costs and increase operational flexibility without discarding the value of LLMs for where their generalist capabilities are indispensable. This perspective reflects a shift toward heterogeneous ecosystems where SLMs handle the bulk of routine work and LLMs are invoked for more complex, open-ended challenges. For organizations ready to adopt this approach, NVIDIA offers tools and models designed to support the transition, including Nemotron and NeMo for end-to-end model lifecycle management. NVIDIA Dev Blog.

What’s new

The article situates SLMs as the central operational workhorses in agentic AI, supported by a growing ecosystem of specialized models and tooling. It highlights real-world advantages such as:

• A 9B-parameter SLM, Nemotron Nano 2, that delivers competitive performance on commonsense reasoning, tool calling, and instruction following, with 128k token contexts and high throughput on a single GPU using open weights and enterprise-ready documentation.
• Demonstrated cost advantages: running a Llama 3.1B SLM can be 10x to 30x cheaper than running a larger sibling (Llama 3.3 405B) under typical configurations, illustrating substantial efficiency gains for routine workloads.
• The practical feasibility of edge deployments and privacy-preserving inference, as SLMs can operate locally (e.g., on consumer-grade GPUs) with solutions like NVIDIA ChatRTX.
• The continued relevance of LLMs for open-domain conversation and cross-domain reasoning, reinforcing a hybrid model where the strongest tool is chosen for the task. These points show that a modular, task-specific approach—combining SLMs and selective LLM calls—can deliver faster, cheaper, and more reliable agentic workflows. NVIDIA Dev Blog.

Why it matters (impact for developers/enterprises)

• Cost reduction and sustainability: SLMs offer substantial savings and reduced power usage for many routine agent tasks, expanding who can participate in building agentic AI.
• Flexibility and reliability: SLMs are easier to fine-tune for strict output formats and exact schemas, reducing the risk of malformed outputs in production.
• Modularity and scalability: A heterogeneous system—specialized SLMs handling core subtasks with LLMs invoked for broader capabilities—matches how agents decompose complex problems.
• Faster iteration and edge deployment: Fine-tuning a new capability on an SLM can take only a few GPU hours, enabling rapid experimentation and local, privacy-preserving inference on edge devices.
• Industry-wide accessibility: As SLM-based pipelines mature, more organizations can participate in developing agentic AI, democratizing workflow automation and innovation.
• The future of agentic AI is not a replacement of LLMs but a shift to a modular ecosystem where the right-sized model is used for the right subtask.

Technical details or Implementation

The path to adopting SLM-enabled agents follows a practical, data-driven process:

• Collect usage data from agents to identify recurring tasks and subtasks.
• Curate and filter data to remove sensitive information, then cluster tasks into categories such as parsing, summarization, or coding.
• Match each task category to candidate SLMs, selecting the model sizes and configurations that best fit performance, cost, and reliability needs.
• Fine-tune selected SLMs using efficient methods such as LoRA or QLoRA to create task-specific experts.
• Gradually delegate more subtasks to cheaper, faster SLMs, evolving an agent from LLM-reliant to a modular, SLM-enabled system.
• Leverage NVIDIA NeMo to curate data, customize and evaluate models, ground agent responses, and monitor and optimize agentic AI systems. This tooling aims to make it feasible for non-specialists to deploy heterogeneous systems in practice.
• Consider edge deployments (e.g., NVIDIA ChatRTX) to run SLMs locally, enabling privacy-preserving, low-latency inference.
• Maintain a hybrid architecture where LLMs remain available for broad, high-complexity tasks while SLMs handle the majority of routine flows. The source stresses that the transition is not about abandoning LLMs but about architectural pragmatism: deploy the right tool for the right job, and use a modular approach to decomposing problems. For more context on these ideas and the underlying benchmarks, see the NVIDIA position paper. NVIDIA Dev Blog.

Key takeaways

• SLMs are effective for many recurring agentic tasks due to their focused capabilities and lower cost.
• A heterogeneous system, combining SLMs for core subtasks with LLMs for selective tasks, offers greater efficiency and flexibility.
• Nemotron Nano 2 demonstrates that small models can achieve strong performance with high throughput and large context windows.
• Fine-tuning agility (LoRA/QLoRA) enables rapid addition of new skills and behavior corrections on SLMs.
• NVIDIA NeMo and edge solutions like ChatRTX support end-to-end tooling and local deployment for practical adoption.

References

• https://developer.nvidia.com/blog/how-small-language-models-are-key-to-scalable-agentic-ai/
• NVIDIA NeMo and Nemotron references mentioned in the source document