SLMs for Agentic AI: Why Smaller Models Outperform LLMs

The burgeoning sector of agentic artificial intelligence, currently valued at over $5.2 billion and projected to soar to $200 billion by 2034, heralds an era where AI will become as ubiquitous as the internet. Yet, this rapid expansion faces a foundational challenge: its reliance on massive, power-hungry Large Language Models (LLMs). While LLMs boast impressive, near-human capabilities, they often represent an inefficient, “sledgehammer-to-crack-a-nut” approach for specialized tasks, leading to exorbitant costs, significant energy waste, and stifled innovation.

However, a compelling alternative is emerging. Research from NVIDIA, detailed in their paper “Small Language Models Are the Future of Agentic AI,” champions Small Language Models (SLMs) as a more intelligent and sustainable path forward. An SLM is defined as a language model compact enough to operate on common consumer electronic devices, performing inference with sufficiently low latency for practical use in single-user agentic requests. As of 2025, this generally encompasses models with fewer than 10 billion parameters. The paper posits that SLMs are not merely a viable alternative to LLMs but, in many scenarios, a superior choice, underpinned by their surprising power, economic advantages, and inherent flexibility.

It is easy to underestimate SLMs, given the long-standing “bigger is better” paradigm in AI. Yet, recent advancements demonstrate that smaller models can match or even surpass the performance of their larger counterparts across a diverse range of tasks. Microsoft’s Phi-2, for instance, with just 2.7 billion parameters, achieves commonsense reasoning and code generation scores comparable to 30-billion-parameter models, while running approximately 15 times faster. The 7-billion-parameter Phi-3 small model extends this, rivalling models up to ten times its size in language understanding, reasoning, and code generation. Similarly, NVIDIA’s Nemotron-H family, ranging from 2 to 9 billion parameters, delivers instruction following and code generation accuracy on par with dense 30-billion-parameter LLMs at a fraction of the inference cost. Even Huggingface’s SmolLM2 series, with models from 125 million to 1.7 billion parameters, can achieve performance akin to 14-billion-parameter models of the same generation, and even 70-billion-parameter models from just two years prior. These examples underscore a clear message: with modern training techniques, sophisticated prompting, and agentic augmentation, performance is not solely dictated by scale.

The economic argument for SLMs is particularly compelling. In terms of inference efficiency, serving a 7-billion-parameter SLM can be 10 to 30 times cheaper than serving a 70 to 175-billion-parameter LLM, considering latency, energy consumption, and computational operations (FLOPs). This translates to real-time agentic responses at scale without prohibitive costs. Furthermore, the agility of fine-tuning SLMs allows for rapid iteration and adaptation—a new behavior or bug fix can be implemented in hours rather than weeks. SLMs also enable edge deployment, running directly on consumer-grade GPUs, which facilitates real-time, offline agentic inference with reduced latency and enhanced data control. This opens new possibilities for on-device AI. Moreover, SLMs foster a modular system design, allowing developers to combine smaller, specialized models for different tasks, akin to building with Lego bricks. This approach is not only more cost-effective but also easier to debug and deploy, better aligning with the operational diversity of real-world AI agents.

The world is not a one-size-fits-all environment, and neither are the tasks assigned to AI agents. This is where SLMs’ flexibility truly excels. Their smaller size and lower training costs enable the creation of multiple specialized expert models tailored for distinct agentic routines. This adaptability allows for seamless responses to evolving user needs, easy compliance with changing regulations across different markets without retraining a monolithic model, and the democratization of AI by lowering the barrier to entry for a broader range of participants and organizations.

Despite the strong case for SLMs, the industry remains heavily invested in LLMs. The NVIDIA paper identifies three primary barriers to SLM adoption: the substantial upfront investment already made in centralized LLM inference infrastructure, a historical focus within the AI community on generalist benchmarks that favor larger models, and a general lack of awareness due to less marketing and press attention compared to LLMs. However, these obstacles are not insurmountable. As the economic benefits of SLMs become more widely recognized, and as new tools and infrastructure emerge to support them, a gradual shift towards an SLM-centric approach is anticipated.

The paper even provides a practical six-step roadmap for converting agentic applications from LLMs to SLMs. This process begins with securing usage data collection by logging all non-human-computer interaction agent calls, including input prompts and output responses. This is followed by meticulous data curation and filtering to remove sensitive information and prepare datasets for fine-tuning. The next step involves task clustering to identify recurring patterns of requests or internal agent operations, which helps define candidate tasks for SLM specialization. Subsequently, the best SLM for each identified task is selected based on capabilities, performance, licensing, and deployment footprint. This leads to specialized SLM fine-tuning using the task-specific datasets. The final step involves continuous iteration and refinement, where SLMs and the routing model are regularly retrained with new data to maintain performance and adapt to evolving usage patterns. This actionable plan offers a clear pathway for organizations to begin harnessing the advantages of SLMs today.

The AI revolution is upon us, but its sustainable scalability cannot be achieved through energy-intensive LLMs alone. The future of agentic AI will instead be built on SLMs—small, efficient, and inherently flexible. NVIDIA’s research serves as both a wake-up call and a practical roadmap, challenging the industry’s LLM obsession while demonstrating that SLMs can deliver comparable performance at a fraction of the cost. This paradigm shift extends beyond technology, promising a more sustainable, equitable, and innovative AI ecosystem. The forthcoming wave of SLMs is even expected to drive hardware innovation, with reports indicating NVIDIA is already developing specialized processing units optimized specifically for these compact powerhouses.