· Generative AI · 1 min read
The Rise of SLMs: Why Mistral and Phi are Stealing the Show
You don't always need a Ferrari to go to the grocery store. Small Language Models (SLMs) are faster, cheaper, and private.
For a long time, the logic was “Bigger is Better.” GPT-4 is massive. Claude 3 Opus is massive. But for 90% of business tasks—summarisation, classification, extraction—these models are inefficient. It is like hiring a Physics PhD to make coffee.
Enter the SLM (Small Language Model)
Models like Mistral 7B, Microsoft Phi-3, and Google Gemma are tiny.
- Run Locally: They can run on a decent laptop or a cheap GPU.
- Privacy: You can run them inside your own VPC (Virtual Private Cloud). No data ever leaves your perimeter.
- Speed: They generate tokens 10x faster than the giants.
The Unit Economics
Calling GPT-4 for every user request is expensive. Fine-tuning a Mistral model for your specific task (e.g. “Extract Name and Date from this PDF”) allows you to achieve GPT-4 level accuracy at 1/100th of the inference cost.
The Future is Hybrid
We are moving to a world where a “Router” model decides:
- “Is this a hard philosophy question? Send to GPT-4.”
- “Is this a simple data extraction? Send to Mistral.”
Optimise your AI spend. Let us deploy efficient, private SLMs for your enterprise. Contact us.
