Thread by @heyshrutimishra

AI Industry Made $57 Billion Mistake and No One’s Talking About It.

While GPT-5 headlines kept you distracted...

NVIDIA quietly released a bold claim:
→ Small Language Models (SLMs) are the future of AI agents

Cheaper, faster and just as capable for 80% of real-world tasks.

Easily one of the biggest shifts in AI this year and most people missed it.

99% people haven’t read this but they should: 🧵

1/The paper is titled:

“Small Language Models are the Future of Agentic AI”

Published by NVIDIA Research.

It challenges the core assumption behind every LLM deployment today:

"That you need GPT-4–level models to run useful agents."

As per the research.. truth is - "You don't.."

now, let's dive deeper:

2/ LLMs like GPT-4 and Claude 3.5 are powerful but most AI agents don’t need that power.

They handle narrow, repetitive tasks.

And SLMs can do those better, cheaper, and faster.

3/ What’s an SLM?

A Small Language Model is tiny enough to run on your laptop or edge device.

We're talking under 10B parameters...fast, fine-tunable, and private.

Think:
→ Lower latency
→ Offline control
→ Fraction of the cost

4/ What did NVIDIA actually say?

In their new paper, NVIDIA Research argues:

“SLMs are the future of agentic AI.”

→ Most AI agents just do narrow, repetitive tasks
→ They don’t need 70B+ parameters
→ SLMs (small language models) are cheaper, faster, and just as accurate in real workflows

Let that sink in!

5/ The math here is wild:

Newer SLMs like Phi-3 and Hymba match 30–70B models in tool use, reasoning, and instruction following:

→ Run 10× faster, use 10–30× less compute in real workflows

Tool use, commonsense, and instruction-following? On point.

6/ Serving GPT-4 is expensive.

Running a tuned SLM is 10–30x cheaper.

And you can deploy them:

→ On-device
→ With custom formats
→ Using tools like ChatRTX, LoRA, QLoRA

Enterprise-ready, budget-friendly.

7/ NVIDIA tested this across 3 real-world AI agents:

- MetaGPT → 60% of tasks replaceable by SLMs
- Open Operator → 40%
- Cradle (GUI automation) → 70%

And those are today’s SLMs.

This paper could reshape how we build AI agents in the next decade.

8/ Why This Matters for AGI:

The path to human-like agents isn’t bigger models.
It’s modular ones.

SLMs can be specialists, like tools in a toolbox.

And that’s exactly how human reasoning works.

9/ The Moral Edge

SLMs aren’t just efficient, they’re ethical.

They:
→ Reduce energy usage
→ Enable edge privacy
→ Empower smaller teams & communities

LLMs centralize power. SLMs distribute it.

10/ So why is nobody using them?

NVIDIA lists 3 reasons:

→ $57B was invested into centralized LLM infra in 2024. But SLMs might now challenge that model, on performance, cost, and flexibility.
→ Benchmarking is still biased toward “bigger is better”
→ SLMs get zero hype compared to GPT-4, Claude, etc.

This paper flips that.

People just don’t know what SLMs can do (yet)

But that’s changing fast.

11/ NVIDIA even outlined a step-by-step migration framework to convert LLM agents to SLM-first systems:

How to migrate LLM-based agents into SLM-first systems
How to fine-tune SLMs for specific tasks
How to cluster tasks and build SLM “skills”
How to scale all locally, if needed

They’re not guessing.
They built the roadmap.

12/ So what does this mean?

→ Most AI agents today are overbuilt
→ You might be paying 20x more for marginal gains
→ You’re also locked into centralized APIs

SLMs break that model.
And NVIDIA just made the case for switching at scale.

13/ This isn’t anti-GPT.

It’s post-GPT.

LLMs gave us the spark.
SLMs will give us the system.

The next 100 million agents won’t run on GPT-4.
They’ll run on tiny, specialized, ultra-cheap models.

And that’s not a theory. It’s already happening.

14/ TL;DR

The AGI race won’t be won with trillion-token giants.

The path to scalable agentic systems isn’t just bigger models. It’s modular, fine-tuned, and specialized powered by SLMs

📄 Read the paper: arxiv.org/abs/2506.02153