Why do people use local LLMs despite the costs?

Individuals value the experience of on-device intelligence, clear hardware and electricity costs compared to cloud APIs, and freedom from censorship or account issues. Enterprises use them for data governance and closed environments.

What are the risks of relying on cloud AI, according to the article?

Cloud AI can become unavailable due to export controls or account suspension, input data may be used for training without opting out, prices can rise, and familiar models may be discontinued. Some also worry about performance degradation before new releases.

Large Language ModelsAI Regulation & PolicyAI Business & IndustryITmedia AI＋Sep 9, 2026Sep 9, 2026, 13:00 JST

ローカルLLM: 'Free but not cheap' — who uses it and why

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3 Key Points

What happened
ITmedia's analysis examines why individuals and enterprises use local LLMs, despite the hardware and electricity costs, focusing on use cases and value.
Why it matters
The article details a shift from relying on cloud AI toward on-premises setups, driven by data governance needs, export control risks, and account suspension concerns.
What to watch
The trend hinges on whether more hospitals, banks, and contractors adopt these systems for efficiency and governance, with real-world deployments at medical institutions and financial firms.

WHO IT HITSEnterprise IT and data governance teams weighing on-premises LLM deployments, plus individual developers and creators who prioritize cost predictability, privacy, or censorship-free experimentation.

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Why do people use local LLMs despite the costs?: Individuals value the experience of on-device intelligence, clear hardware and electricity costs compared to cloud APIs, and freedom from censorship or account issues. Enterprises use them for data governance and closed environments.
What are the risks of relying on cloud AI, according to the article?: Cloud AI can become unavailable due to export controls or account suspension, input data may be used for training without opting out, prices can rise, and familiar models may be discontinued. Some also worry about performance degradation before new releases.

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