GoKawiilIn 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.
First, they ran a series of SLMs (QWEN 3, GEMMA 3, GPT-OSS, GRANITE 4.0) that can be downloaded on a local PC and compared their performance with cloud-based state-of-the-art LLMs (ChatGPT 5, Claude Sonnet 4.5, Gemini 2.5 Pro).
They ran these SLMs on local PCs powered either by an Nvidia chip or an Apple M4 chip, as they are readily available in current high-end desktop computers (the entire study was done before Nvidia presented its AI chip for PCs, which will only accelerate the move away from datacentres to models run on desktops).
Then they traced the performance of these SLMs vs LLM between 2023 and October 2025 on both chat tasks and reasoning tasks.
The chart below shows the Win/Tie-ratio for SLMs vs LLMs in chat requests, which still make up the vast majority of requests today. As you can see, in every domain, the best SLM is able to find the same or better answers than an LLM in 90% or more of the cases, with an average across all domains of 98.6%.
Win/Tie-ratio of SLM vs. LLM in chat requests
Source: Saad-Falson et al. (2026)
When it comes to reasoning tasks, which are obviously more demanding, SLMs are catching up fast. On average, they provide a better or at least as good an answer as LLMs in 62.5% of the cases.
Win/Tie-ratio of SLM vs. LLM in reasoning tasks
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