Private LLM Private, Uncensored AI Chat for iPhone, iPad, and Mac
No Cloud, No Tracking, No Logins.
Run AI Offline on Your iPhone, iPad, and Mac
Private LLM runs entirely on your iPhone, iPad, or Mac. Your conversations never leave the device, and no internet is required after the first model download. No account, no tracking, no logs. One purchase unlocks the app across every Apple device you own and your Family Sharing group.
Run DeepSeek R1, Llama 3.3, Qwen3, and Gemma 3 Locally
Private LLM runs the leading open-source models directly on your Apple devices — DeepSeek R1 Distill, Llama 3.3 70B, Qwen3 4B, Phi 4, Google Gemma 3, and more. Every conversation stays on-device, and every model is quantized in-house for the best possible quality on your hardware.
Local AI in Siri and Apple Shortcuts — No Code
Private LLM plugs directly into Siri and the Shortcuts app. Build AI-driven workflows that summarise text, generate writing, or pipe responses into any of the 70+ apps that support the x-callback-url specification. No code required.
One Purchase, No Subscription — Family Sharing for Six
Ditch the subscriptions for a smarter choice with Private LLM. A single purchase unlocks the app across all Apple platforms—iPhone, iPad, and Mac—while enabling Family Sharing for up to six relatives. This approach not only simplifies access but also amplifies the value of your investment, making digital privacy and intelligence universally available in your family.
AI Writing Tools Built Into macOS
Select any text in any macOS app, right-click, and Private LLM rewrites, summarises, or corrects it — entirely on-device. Supports English and major Western European languages.
Built by Two Engineers, Not VCs
Private LLM is built by two engineers in the EU — bootstrapped, no VC funding, no growth-hacking roadmap. We are the only app on the App Store with OmniQuant and GPTQ quantization, which produce measurably better output than the RTN quantization used by MLX and llama.cpp wrapper apps like Ollama and LM Studio. We answer to users, not investors — which is why your data stays on-device and always will.
From the App Store
Real reviews from iPhone and Mac users
“Absolutely one of the best apps for runnibg some models on your phone instead of sending everything to the cloud.👨🏼💻That makes it useful for learning, private notes, brainstorming, and practicing Al worktlows.💯”
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OmniQuant and GPTQ Quantization: Better Output, Less Memory
Private LLM uses OmniQuant and GPTQ quantization. When LLMs are quantized for on-device inference, outlier weight values hurt text generation quality. OmniQuant modulates outlier weights with a learnable, optimization-based clipping mechanism that minimizes quantization error. GPTQ uses approximate second-order (Hessian) information to minimize reconstruction error on the weights that matter most. The affine RTN quantization used by MLX-based apps like LM Studio, and the block-wise RTN variants used by llama.cpp-based apps like Ollama, skip this kind of per-weight optimization — which is why those apps produce lower-quality output on the same Apple hardware. We constantly explore advanced quantization methods, work that wrapper apps built on third-party inference engines cannot take on. OmniQuant and GPTQ paired with optimized model-specific Metal kernels let Private LLM deliver text generation that is both fast and high-quality on Apple hardware.
Find Your Model
Browse every open-source model Private LLM runs — filter by your device, RAM, and use case, or let the picker recommend one.
How Can We Help?
Whether you've got a question or you're facing an issue with Private LLM, we're here to help you out. Just drop your details in the form below, and we'll get back to you as soon as we can.