#23 – Small Language Models (SLMs) — Why Smaller is Smarter

We’ve spent the last few years obsessed with massive AI models packed with trillions of parameters. But what if the future of artificial intelligence isn’t about getting bigger, but getting smaller, faster, and highly specialized?

In this episode, we dive into the rising trend of Small Language Models (SLMs). We explore why the tech industry is pivoting away from giant, expensive, cloud-dependent AIs toward compact, hyper-focused models that can run entirely on your smartphone or local machine. It turns out, when it comes to AI, bigger isn’t always better.

In this episode, we unpack:

• The core differences between LLMs (Large) and SLMs (Small) in terms of training data, size, and capabilities.

• Why companies are actively ditching massive, expensive API calls in favor of fine-tuned local models.

• The huge advantages of going small: zero latency, drastically lower computing costs, and total data privacy.

• Real-world examples of SLMs outperforming their giant counterparts on specific, everyday tasks.

If you want to understand how AI is becoming more accessible, cheaper, and deeply integrated into our daily devices, this episode is exactly what you need.