
Local LLM-Vision — Fully Offline iOS AI
Run LLM & Vision AI fully offline on your iPhone
What it does
A fully on-device AI app for iOS that runs both large language models (LLMs) and vision-language models (VLMs) entirely offline. Chat with local LLMs or analyze images in real time, all powered by Apple Metal acceleration. Unlike cloud-based AI apps, no data is uploaded and no internet connection is required. You can switch between multiple models depending on speed, size, and reasoning needs. Private, fast, and designed for modern iPhones.
Does the same job
all alternatives →- OAOfflineLLM – a Vision Pro app running TinyLlama on device2024 · apps.apple.com · ▲126
Hey, I built this in a day while at Founders Inc Apple Vision Pro residency. Try it out, let me know what you think.



- SOSwiftAI – open-source library to easily build LLM features on iOS/macOS2025 · github.com · ▲74
We built SwiftAI, an open-source Swift library that lets you use Apple’s on-device LLMs when available (Apple opened access in June), and fall back to a cloud model when they aren’t available — all without duplicating code. SwiftAI gives you: - A single, model-agnostic API - An agent/tool loop - Strongly-typed structured outputs - Optional chat state Backstory: We started experimenting with Apple’s local models because they’re free (no API calls), private, and work offline. The problem: not all devices support them (older iPhones, Apple Intelligence disabled, low battery, etc.). That…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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