A local LLM is a large language model whose files you download once and then run on your own device. It does the same reading, summarising and question-answering as ChatGPT, but it works offline and sends nothing back to the company that made it.

Is a local LLM as good as ChatGPT?

For the most open-ended tasks the largest external models still lead, but the gap has narrowed sharply — from about 8% to under 2% on one widely-used leaderboard between early 2024 and early 2025. For reading and summarising your own documents, a local model is more than capable.

Do you need an internet connection to use a local LLM?

No. Once the model is on your device it runs entirely offline. That is precisely why it is private: with no connection to an outside server, there is nowhere for your documents to be sent.

Are local LLMs safe for confidential documents?

Yes, when the tool is genuinely local. Because the model processes everything on your own machine, your documents are never transmitted to a provider, never retained externally, and never used to train someone else's model.

What Is a Local LLM? Running Large Language Models Privately

Q: Do you need to be technical to use a local LLM?

Not any more. Running a raw model once required developer tools, but finished applications now package a local LLM behind an ordinary interface, so a professional can use one to analyse documents without any setup or technical knowledge.

You have heard that AI can help you prepare for a board meeting — condense a 300-page pack, cross-reference the figures, surface the questions worth asking. You have also heard that sending confidential papers to ChatGPT is a risk. A local LLM is what sits between those two facts.

A local LLM is a large language model that runs entirely on your own computer, offline, with nothing sent to the company that made it. It does the same kind of work as ChatGPT or Claude — reading, summarising, answering questions in plain language — but because the model itself lives on your device, the documents never leave it. This is the technology that makes private, on-device AI possible.

Key takeaways

A local LLM is a large language model you download once and then run on your own device — it works offline and sends nothing back to its publisher.
The gap between freely-downloadable models and the best closed models narrowed from 8.04% to 1.70% on the Chatbot Arena Leaderboard between January 2024 and February 2025, according to Stanford HAI's 2025 AI Index Report.
Models small enough to run on a phone now exist: Microsoft Research reported its Phi-3-mini model running natively and fully offline on an iPhone 14 at more than 12 tokens per second.
Data privacy is the top ethical concern professionals hold about generative AI — 40% ranked it their number-one concern in 2024, up from 25% in 2023, according to Deloitte.
You do not need to be technical: finished applications now package a local LLM behind an ordinary interface, so the privacy benefit no longer requires developer skills.

What is a local LLM, in plain terms?

Start with the two words. A large language model (LLM) is, in Wikipedia's definition, "a neural network trained on a vast amount of text for natural language processing tasks, especially language generation."¹ In practice it is the kind of AI behind ChatGPT: you give it text, it responds with relevant, human-like text.

"Local" describes where that model runs. The technical term is an open-weight model — one whose core files are published so that, as Stanford's Institute for Human-Centered AI puts it, "anyone [can] download it" and "run the model on their own computers."² Download the model once, and it will run on your own machine with no further connection to the outside world. That is the entire distinction: a cloud service keeps the model on its servers and you send your documents to it; a local LLM puts the model on your device and your documents stay where they are.

How is a local LLM different from ChatGPT or Claude?

ChatGPT and Claude are services: the model sits on the provider's external servers, and to use it you transmit your text there for processing. A local LLM is a file: the model sits on your device, and the processing happens without anything being sent anywhere. Same category of tool, opposite data journey.

That offline property is not a side effect — it is the privacy guarantee. As an independent academic review of on-device models notes, "on-device inference... allows models to run directly on the user's device without sending data to a cloud server," Xu et al., 2024.³ If nothing is transmitted, there is no query log at the provider, no retained copy, and nothing that can be exposed in a breach of infrastructure you do not control.

Factor	Cloud LLM service (ChatGPT, Claude)	Local LLM
What it is	A service you connect to	A model file on your device
Where your documents are processed	The provider's external servers	Your own machine
Internet required to use	Yes	No
Documents sent to the provider	Yes	No
Used to improve the provider's model	Possible, depending on settings	No
Best suited to	General, non-confidential tasks	Confidential and offline work

For the full governance case behind that right-hand column, see Local AI vs Cloud AI.

Can a local LLM really run on an ordinary computer?

Yes — and the speed at which that became true has surprised even the experts. Simon Willison, an independent researcher who builds widely-used open-source AI tools, told MIT Technology Review in 2025: "A couple of years ago, I'd have said personal computers are not powerful enough to run the good models. You need a $50,000 server rack to run them. And I kept on being proved wrong time and time again."⁴

The numbers bear him out. On the widely-cited Chatbot Arena Leaderboard, the best closed model led the best open-weight model by 8.04% in January 2024; by February 2025 that lead had shrunk to just 1.70%, according to Stanford HAI's 2025 AI Index Report.⁵ Models have also become dramatically more efficient: Microsoft Research reported that its compact Phi-3-mini model ran "natively" and "fully offline" on an iPhone 14, generating more than 12 tokens per second.⁶ If a capable model fits on a phone, an ordinary laptop or desktop has ample room for one geared to reading documents.

Which local LLMs are there?

"Local LLM" is a category, not a product. Several major organisations now release open-weight models that anyone can download and run offline, at no licence cost. These are the families a professional is most likely to encounter behind a finished tool:

Model family	Released by	Runs offline on ordinary hardware
Llama	Meta	Yes — the compact 1B/3B versions are built for on-device use
Mistral	Mistral AI (France)	Yes — designed for local and on-premise deployment
Gemma	Google DeepMind	Yes — downloadable weights, run via standard local tools
Phi	Microsoft	Yes — small enough to run on a phone, per Microsoft Research
Qwen	Alibaba Cloud	Yes — open-weight releases under a permissive licence
DeepSeek	DeepSeek	Yes — open-weight, with smaller versions for lower-spec machines

Adoption of these downloadable models has been substantial: Meta announced in March 2025 that its Llama models had passed one billion downloads,⁷ and Google reported its Gemma models had been downloaded more than 400 million times.⁸ (Both are the vendors' own figures.) The practical point for a non-technical reader is simpler than the list suggests: all of these can be downloaded once and then run entirely on a local machine, with no further internet connection and no material sent back to the publisher.

How do you actually run a local LLM?

There are three routes, and only one of them asks anything technical of you.

The first is developer tooling — programs such as Ollama or LM Studio that let a technically-confident user download a model and run it from their own machine. These are capable and free, but they assume you are comfortable installing and configuring software.

The second is a finished, purpose-built application — an offline assistant that bundles a local model behind an ordinary interface. You open it, add your documents, and ask questions in plain language; there is no model to install and nothing to configure. This is the route that matters for most professionals, and the one that turns a local LLM from an enthusiast's project into a working tool.

The third, increasingly, is the device itself, as on-device AI features arrive built into phones and laptops. These are convenient but general-purpose, and are not designed for the confidentiality demands of board and governance material.

For reading board papers privately, a dedicated offline application is the practical choice: it gives you the confidentiality of a local model without asking you to become an engineer.

Why would a professional — not a developer — want one?

Because the reason to run a model locally is not technical curiosity; it is confidentiality. Data privacy is the single biggest ethical concern professionals hold about generative AI — 40% ranked it their number-one concern in 2024, up from 25% the year before, according to Deloitte's State of Ethics and Trust in Technology report.⁹ A local LLM answers that concern at the root: if the model runs on your device, the confidential material you feed it has nowhere else to go.

There is a governance argument for open, locally-run models too. Arthur Mensch, cofounder and chief executive of Mistral AI, wrote in a 2026 LinkedIn post, as reported by Sifted, that the company exists "to make sure that everyone gets access to the best AI systems, outside of centralised control exercised by states or corporations…" — the appeal being deployment where documents never leave the user's own infrastructure.¹⁰

The old catch was that running a raw model demanded developer tools. That has changed. Finished applications now package a local LLM behind an ordinary interface — which is exactly what meetinginsight.ai does. It runs a local model on your own device so you can add board papers and ask questions of them in plain language, with the model, the documents and the answers all staying on the machine in front of you. You get the confidentiality of a local LLM without ever having to set one up. For how that fits a preparation routine, see our guide to reading a board pack.

What are the trade-offs of running an LLM locally?

Two, stated plainly. First, the very largest frontier models — the ones needing that server rack — remain more capable than anything you can run at home on the broadest, most open-ended tasks; a local model is a deliberate trade of a little raw power for complete privacy. Second, a local model is only as private as the application around it: "local" is the right foundation, but it is a well-built offline tool — one that genuinely sends nothing out — that delivers the confidentiality, not the label. The test to apply to any product is blunt: does anything in my documents ever leave this device? For a genuine local LLM, the answer is no.

In summary

A local LLM is a large language model that runs on your own device, offline, sending nothing to the company that built it. It has become capable enough to read and reason over your documents, small enough to run on ordinary hardware, and — thanks to finished applications — usable without any technical skill. For anyone handling confidential material, it is the technology that lets you get real work from AI while the documents stay entirely under your control.

For the broader picture, read What Is Local AI?; for the head-to-head, read Local AI vs Cloud AI. Or simply try it: meetinginsight.ai runs a local model on your device to analyse board papers with nothing sent or stored elsewhere. Start a free 30-day trial at meetinginsight.ai/download.

Notes

meetinginsight.ai processes your board papers entirely on your device. Nothing sent. Nothing stored elsewhere. Download a free 30-day trial at meetinginsight.ai/download.

Footnotes

Wikipedia, "Large language model." https://en.wikipedia.org/wiki/Large_language_model ↩
Stanford Institute for Human-Centered AI (HAI), "What is an Open-Weight Model?" https://hai.stanford.edu/ai-definitions/what-is-an-open-weight-model ↩
Jiajun Xu et al., "On-Device Language Models: A Comprehensive Review," arXiv, 2024. https://arxiv.org/abs/2409.00088 ↩
Simon Willison, quoted in "How to run an LLM on your laptop," MIT Technology Review, 17 July 2025. https://www.technologyreview.com/2025/07/17/1120391/how-to-run-an-llm-on-your-laptop/ ↩
Stanford HAI, "2025 AI Index Report — Technical Performance," 2025. https://hai.stanford.edu/ai-index/2025-ai-index-report/technical-performance ↩
Microsoft Research, "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone," 2024. https://www.microsoft.com/en-us/research/publication/phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/ ↩
Meta, "Celebrating 1 Billion Downloads of Llama," 18 March 2025 (Meta's own figure). https://about.fb.com/news/2025/03/celebrating-1-billion-downloads-llama/ ↩
Google, "Gemma" announcement, 2026 (Google's own figure). https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/ ↩
Deloitte, "State of Ethics and Trust in Technology," 3rd annual report, September 2024. https://www.deloitte.com/us/en/about/governance/technology-trust-ethics-annual-report.html ↩
Arthur Mensch, cofounder and CEO of Mistral AI, LinkedIn post as reported by Sifted, 17 June 2026 (quote truncated; full sentence continues "…that feel the need to control in-fine deployment of AI"). https://sifted.eu/articles/mistral-arthur-mensch-open-source-anthropic ↩