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
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