- Published on July 9, 2025
- In Global Tech
Small Language Models Make More Sense for Agentic AI
An SLM should be small and efficient enough to run locally on a laptop, smartphone, or personal GPU, while still being fast and useful for real-world AI agent tasks.
- By Siddharth Jindal
There is a common misconception that the bigger LLMs are, the better they would be. Since the emergence of large language models (LLMs) like GPT-4 and Claude, AI labs and researchers have been racing to build ever-larger systems with more parameters, greater computing demands and higher costs.
For instance, OpenAI, SoftBank, and Oracle plan to spend $500 billion on The Stargate Project to build a network of AI data centres and support energy infrastructure in Texas and other locations. The goal is to expand the computing capacity required to develop and run advanced AI models, particularly those created by OpenAI—namely ChatGPT.
On the other hand, Meta is on a hiring spree to build superintelligence.
However, a recent position paper from NVIDIA Research presents a provocat
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Siddharth Jindal
Siddharth is a media graduate who loves to explore tech through journalism and putting forward ideas worth pondering about in the era of artificial intelligence.
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