Traditionally, the development of large language models (LLMs),

Required immense computational resources,

Resulting into high costs, often in the tens of millions of dollars.

However, the recent advancements in AI have disrupted this paradigm,

Raising questions about sustainability, performance,

And the long-term viability of these models,

especially regarding computational costs associated with inference.

Innovative methods are proving that smaller,

More curated datasets, coupled with smarter training techniques,

Yielding similarly high-performing models at a fraction of the cost.

This shift represents a new frontier in AI development.

Where AI labs increasingly prioritize efficiency over sheer scale.

Bal Krishna, Editor

bal@mycoordinates.org