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Private, offline and yours: how to search the literature with local AI systems
David Brzostowicki is a freelance writer in Fort Lauderdale, Florida.
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When he wants to scratch an intellectual itch, Ian Gaudet does what people are increasingly doing on the Internet: he asks a chatbot. But when it comes to his research data, Gaudet, a neuroscientist at Florida Atlantic University in Boca Raton, prefers to stay offline. “I do not want Claude to start telling people my research results,” he explains.
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Cloud-based large language models (LLMs), such as those underlying the chatbots Claude (Anthropic), Gemini (Google) and ChatGPT (OpenAI), represent the cutting edge of artificial-intelligence performance. However, these systems might use what is typed into them for training purposes. For researchers such as Gaudet, state-of-the-art performance isn’t worth the risk to data protection, reproducibility or budgets. Instead, they turn to a growing collection of tools, such as Ollama and LM Studio, that enable researchers to download LLMs to their computer and use them offline.
These programs can help researchers to sort and query their own literature libraries to gather insights, for instance. But two fresh barriers have replaced the programming and set-up issues that once kept researchers from using them: the hardware needed to run them has become much more expensive, and the tools’ output still requires scrutiny to assess its veracity.
Ask researchers why they want to run an AI model on their desktop rather than in the cloud, and the answer almost always starts with data. Eric Böhnisch-Volkmann, president of DEVONtechnologies in Coeur d’Alene, Idaho, hears it constantly from users of the firm’s document-management software DEVONthink, which integrates LLMs.
“Medical records is one thing, lawsuits is one other thing, research is another,” he says. “They just want to keep their hands on [their data] and not give it to someone else they don’t trust.”
Privacy concerns are well founded. A 2025 analysis found that the six AI developers studied — Amazon, Anthropic, Google, Meta, Microsoft and OpenAI — trained their models on users’ input data by default (J. King et al. Preprint at arXiv https://doi.org/g967b4; 2025).
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