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Local LLM App by Ente

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Why This Matters

The release of Ente's local LLM app, Ensu, marks a significant step towards empowering users with private, offline AI models that do not rely on big tech. This development addresses privacy concerns, reduces dependency on centralized providers, and enhances user control over AI interactions, which is crucial for the future of responsible AI deployment in the tech industry and for consumers alike.

Key Takeaways

LLMs are too important to be left to big tech. There is a gap between frontier models and models that can run on your device, but local models improve each day, and once they cross a certain capability threshold, they will be good enough for most purposes; and will come with full privacy and control.

Based on these assumptions, we've been working on Ensu, Ente's offline LLM app. Today is our first release.

Download it here.

In the rest of this post, we'll explain why we think the assumptions hold, what we're doing, and how you can get involved.

Why

LLMs are too important to be left to big tech. We've written in depth about this earlier, here and here.

Briefly, those posts come at it from two angles:

If you're someone who hates LLMs, you would still be able to recognize in clearer moments of thought that LLMs are a technology that can't just be wished away. If you're someone who finds joy in interacting with LLMs, you would recognize the lack of privacy and the high dependency (arbitrary bans, content shaping, non-portable memory) you have on centralized providers.

And in both cases it is also clear that LLMs can be used to manipulate people en masse. Ergo, we can't be at the mercy of big tech controlling them.

The issue is that there is a capability gap between large centralized models and smaller models that can be run offline on your device.

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