If this is true, the hyperscalers are toast
New research suggests that small language models (SLMs) running on local hardware may soon outperform large language models (LLMs) hosted in data centers. This shift could potentially disrupt the massive infrastructure investments currently being made by hyperscalers.
Why it matters
If accurate, this trend would fundamentally change the economics of AI development and the necessity of massive cloud computing infrastructure.
In my regular research (behind a paywall), I have been saying for a while that I think the future of AI is not large language models (LLM), but small language models (SLM) run on local desktop computers or even mobile phones. In May, a team from Stanford University published research that compared these SLMs with the performance of LLMs run in data centres. If their results are true, then we will hardly need any data centres in the future, and the hyperscalers are wasting hundreds of billions of dollars in investments.
Seriously, if you are an investor trying to figure out where to invest in the AI hype, you need to read this paper in full. But to get you started, let me give you some highlights.
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