Small Language Models: Why “Smaller” is the Next Big Thing in AI

Small Language Models (SLMs) are AI models designed to be lightweight enough to run on everyday hardware like laptops, desktops, or even phones. They typically have under ~10 billion parameters, making them faster, cheaper, and more private than massive cloud-based Large Language Models (LLMs). Despite being smaller, modern SLMs are powerful enough to handle most everyday AI tasks, from reasoning to coding to document summarization.

Big AI models — the ones making headlines — often have hundreds of billions of parameters and run only in giant data centers. These Large Language Models (LLMs), like GPT or Gemini, are powerful, but they come with heavy costs: expensive infrastructure, slower response times, and serious privacy concerns.

Now, a new wave is here: Small Language Models (SLMs). These models don’t try to be “know-it-all” giants. Instead, they’re designed to be fast, efficient, and practical — and they may power the future of AI in your phone, your laptop, and your workplace.

In fact, recent papers — from NVIDIA researchers, Ruslan Popov’s team, and Michael Liut at the University of Toronto — all make the case that SLMs aren’t just a side-story. They’re about to become the main act.

What Exactly Are SLMs?

When people hear the term “Small Language Model”, the natural question is: “how small is small?”

The NVIDIA research team gives us a clear anchor:

An SLM is any AI model that can run on consumer hardware (like a laptop or desktop with a decent GPU) and respond quickly enough for one person’s use.

As of 2025, that usually means under ~10 billion parameters.

To put this in perspective:

• Large Language Models (LLMs), like GPT-5 or Gemini Ultra, can have hundreds of billions (even approaching a trillion) parameters. Running them requires data centers packed with specialized chips, costing millions to operate.
• Small Language Models (SLMs), by contrast, typically have millions to a few billion parameters. That’s still a lot, but they’re light enough to run on a consumer laptop, workstation, or even a smartphone — no massive server farm required.

Think of it like this:

• LLMs are like renting time on a supercomputer in the cloud — extremely powerful, but expensive, slower to access, and you don’t control the hardware.
• SLMs are like having a high-end laptop or smartphone AI assistant in your hands — smaller, but still plenty capable for day-to-day jobs.

SLMs are AI tools that strike the balance between power and efficiency — not too big, not too weak, but “just right” for practical use.

And Ruslan Popov’s paper adds another useful detail: SLMs aren’t a separate species of AI — they use the same transformer architecture as LLMs, just scaled down for speed and footprint. This means they still generate text in the same way (predicting the next word based on context), but they’re optimized for real-world usability instead of raw leaderboard scores. And there’s an important philosophical point: “small” doesn’t mean “lesser.” NVIDIA’s team stresses that the label SLM is about deployment context — speed, memory, privacy — not just parameter count. What matters is whether the model actually works well for your task and your hardware.

👉 SLMs are designed for the devices and problems most of us actually use every day — fast, lean, and fit-for-purpose.

Table 1: LLMs vs. SLMs — A Simple Analogy

Before diving into specific model families, it helps to see the big-picture differences between Large Language Models (LLMs) and Small Language Models (SLMs).

Table 1 compares them side by side, showing where each type of model runs, how many parameters they usually have, and what they’re best suited for.
LLMs excel at open-ended, complex reasoning, but they require heavy infrastructure.
SLMs, on the other hand, are built for speed, affordability, privacy, and everyday practical tasks — making them a better fit for most real-world scenarios.

Feature	Large Language Models (LLMs) 🏢	Small Language Models (SLMs) 💻
Where they run	Data centers, cloud servers	Laptops, desktops, even phones
Parameters	100B–1T+	Millions – <10B
Response speed	Slower (network + compute)	Faster, real-time
Cost	Expensive to run & maintain	Affordable, even free locally
Training time	Weeks to months	Hours to days
Privacy	Data leaves your device	Runs fully local, private
Energy use	Very high (big carbon footprint)	Low, greener AI option
Best for	Open-ended, complex reasoning	Everyday tasks, tool use, domain-specific jobs

SLMs are becoming powerful

As the research shows, SLMs are becoming powerful enough for most day-to-day AI work. But here’s the catch: whether you’re using a Small Language Model on your laptop or a Large Language Model in the cloud, a model is only as useful as the knowledge, context, and tools you give it.

This is where Feluda.ai comes in.

Feluda isn’t another model — it’s the knowledgebase and skill layer that any AI can tap into. Instead of locking you into one provider, Feluda gives you freedom:

• Run a local SLM for speed, privacy, and cost savings (as highlighted by Popov’s work on local-first AI).
• Switch to a cloud LLM when you need deep reasoning or creativity (the kind of escalation NVIDIA recommends in their hybrid-agent design).
• Either way, the model can draw on the same Feluda genes — bundles of prompts, tools, resources, and domain expertise that act like plug-in skills.

Because Feluda is vendor-agnostic, you avoid lock-in. You can experiment freely, mix and match models, and keep building your AI capabilities knowing your knowledge layer stays consistent.

In a world where both SLMs and LLMs have their place, Feluda makes sure your AI — whichever size you choose — has the brains, context, and expertise to be truly useful.

SLM Performance and Trends

One of the most surprising findings across the recent research is just how good modern SLMs have become.

In benchmark studies, today’s small models regularly hit 60–75% accuracy on reasoning and math tasks. That may not sound like a perfect score, but it puts them in the same league as older “flagship” LLMs from just a few years ago. In other words: what used to require a giant cloud model in 2021 can now be done on your laptop in 2025.

And in certain domains — like tool use, structured workflows, and even code generation — SLMs are already rivaling or surpassing much larger models. NVIDIA’s team showed that around 40–70% of tasks in popular open-source agents can be offloaded to SLMs without losing quality, which translates directly into faster performance and lower bills.

The Leading Families of SLMs

A handful of model families dominate today’s open SLM ecosystem:

• Qwen (Alibaba) → Known for strong performance in reasoning and math, widely adopted in Asia.
• Gemma-2 (Google) → A family released in multiple sizes (2B, 9B, 27B) and designed for lightweight deployment, with the 2B model often outperforming expectations.
• Phi (Microsoft) → Focused on efficiency and compactness, tuned heavily for reasoning tasks.
• Llama (Meta) → Popular in the open-source community, spawning countless fine-tuned variants.

Table 2: Overview of Lightweight and Mid-Scale Open-Source LLMs

To make the landscape more concrete, Table 2 highlights the leading families of Small Language Models (SLMs) available today.
They range from ultra-compact models built for edge and mobile devices, to mid-scale architectures optimized for reasoning, multilingual use, or efficient local deployment.

Each entry shows the parameter size, best-fit category, and a short note on where the model shines — plus a direct link to explore it further.

This way, you can quickly match the right model to your task, hardware, and privacy needs.

Model	Parameters	Category	Best For / Notes	Access Link	Open Source
Qwen2	0.5B, 1B, 7B	General NLP / Scalable	Lightweight (0.5B) for apps; 7B for summarization & text generation. Fast & efficient for resource-limited apps.	HuggingFace	Yes
Mistral Nemo	12B	Advanced NLP	Complex NLP (translation, real-time dialogue). Balances complexity & practicality. Competes with Falcon 40B.	HuggingFace	Yes (Apache 2.0)
Llama 3.1	8B	General NLP	Great balance between power & efficiency. Strong at Q&A and sentiment analysis.	Ollama	Yes (restrictions)
Pythia	160M – 2.8B	Reasoning / Coding	Reasoning & coding. Transparent training. Stronger than GPT-Neo in structured tasks.	GitHub	Yes
Cerebras-GPT	111M – 2.7B	Efficient NLP	Efficient, Chinchilla-scaling. Optimized for low-resource environments.	GitHub	Yes
Phi-3.5	3.8B	Long Context / Reasoning	128K context, multilingual, long documents, reasoning. Strong alternative to GPT-3.5.	HuggingFace	Yes (research only)
TinyLlama	1.1B	Edge / Mobile	Very efficient. Stronger than Pythia-1.4B at commonsense reasoning. Optimized for mobile/edge devices.	GitHub	Yes
Gemma2	9B, 27B	General NLP / Local	Lightweight for local deployment, translation, real-time tasks.	Google AI	Yes (permissive)
OpenELM	270M – 3B	On-Device AI	Energy-efficient on-device AI (Apple). Great for multitasking & real-time mobile use.	GitHub	Yes
DCLM	1B, 7B	Reasoning	Optimized for commonsense reasoning. Competes with LLaMA 2 (7B) & Mistral 7B.	GitHub	Yes

Training Beyond Size

Traditionally, the “Chinchilla law” suggested that model performance is best when the ratio of parameters to training tokens is around 1:20. But SLM developers are breaking that rule: many newer SLMs are trained on far more tokens than expected.

Why? Because when your model is smaller, feeding it more diverse data helps it overcome its size limits. Instead of relying purely on scale, SLM builders now rely on training recipes, data quality, and clever fine-tuning methods to close the gap with LLMs.

This shift signals an important trend: The future of performance isn’t just about who has the biggest model. It’s about who trains smarter.

The Bottom Line on Performance

SLMs may not yet match the very largest models on open-ended creative reasoning. But in most real-world workflows — customer service, document analysis, education, or coding support — they’re already “good enough” to be the default choice.

And as the NVIDIA paper reminds us, if you build your system with hybrid routing (SLM-first, escalate only when needed), you get the best of both worlds: speed, privacy, and cost savings — without losing peak capability.

Are They Good Enough?

This is the question most people ask when they first hear about Small Language Models: “Sure, they’re smaller and faster — but can they actually do the job?”

The short answer, supported by multiple research papers, is: yes.

Evidence from NVIDIA

NVIDIA’s position paper highlights a number of striking cases where SLMs outperform expectations. For example, Microsoft’s Phi-3 model (7B parameters) matches — and sometimes even beats — much larger models on tasks like reasoning, problem solving, and coding. Even more impressive, it does this while running up to 15 times faster than its heavyweight counterparts. This shows that efficiency and performance aren’t mutually exclusive — with the right training recipe, small can be mighty.

Evidence from Popov’s Experiments

Ruslan Popov and his team took an even more approachable approach: they asked 2–3B parameter models like Google’s Gemma-2 and Meta’s Llama 3B a series of sanity-check, common-sense questions. The results? These lightweight models got most of the answers right — reliably handling tasks like basic math, object recognition, and everyday reasoning. Popov even noted that Google’s Gemma-2 2B model sometimes outperformed larger peers, showing that architecture and training matter just as much as raw size.

Everyday “Good Enough”

Put simply, SLMs can now handle the bread-and-butter AI tasks most people need:

• Summarizing documents
• Answering common knowledge questions
• Assisting with code snippets
• Running structured workflows
• Supporting customer service interactions

And thanks to their smaller size, they do this faster, cheaper, and more privately than LLMs.

The Catch: Open-Ended Creativity

Of course, there are still limits. Open-ended tasks like writing a novel, conducting long multi-step reasoning, or producing highly nuanced conversation often benefit from larger LLMs. This is why NVIDIA emphasizes hybrid systems: start with an SLM for speed and privacy, escalate to an LLM only when you truly need it.