PrivateLLM
PrivateLLM Private LLM is a versatile AI chatbot designed for privacy, convenience, and creativity. It operates offline on your Apple devices, ensurin...
Last verified:
What is PrivateLLM?
PrivateLLM Private LLM is a local and private AI chatbot that runs entirely on your iPhone, iPad, and Mac without requiring internet connectivity after the initial model download. The app enables users to run leading open-source large language models including DeepSeek R1 Distill, Llama 3.3 70B, Qwen3, Phi 4, and Google Gemma 3 directly on their Apple devices. Every conversation stays on-device with no cloud processing, no tracking, no logins, and no account required, ensuring complete data privacy and security.
Key features include advanced OmniQuant and GPTQ quantization techniques that produce measurably better text output quality compared to competing apps using RTN quantization. The app supports Siri and Apple Shortcuts integration for building AI-driven workflows without code, macOS right-click AI writing tools for rewriting/summarizing/correcting text in any app, 8K token context length on iOS and 32K on Mac, and Family Sharing for up to six people. Models are quantized in-house by the developers for optimal quality on each hardware configuration.
Private LLM is designed for privacy-conscious users who want powerful AI assistance without data leaving their device, developers who need offline AI for restricted environments, content creators and writers needing AI-powered writing tools, and anyone tired of subscription-based AI services. The app is built by two bootstrapped engineers in the EU with no VC funding, answering to users rather than investors.
The app supports a vast library of open-source models across multiple families (Llama 3/3.2/3.3, Qwen 2.5/3, Gemma 2/3, DeepSeek R1 Distill, Phi 3/4, Mistral, Mixtral) with specialized variants including uncensored versions, coding-focused models, biomedical models, role-play models, and multilingual models for German, Hebrew, Japanese, Chinese, and over 140 languages with Gemma 3.
PrivateLLM pricing
Pricing model: Paid
One-time purchase on the App Store with no subscription fees. A single purchase unlocks the app across all Apple platforms (iPhone, iPad, and Mac) and enables Family Sharing for up to six relatives. The exact price is not listed on the website but must be viewed on the App Store. There is no free tier available. The one-time payment model means no recurring fees, no hidden clauses, and no restrictions on commercial or production use.
PrivateLLM pros
- Runs entirely offline with no internet required after initial model download
- No account, no tracking, no logs, no cloud processing
- One-time purchase with no subscription fees
- Family Sharing supports up to 6 family members
- Works across iPhone, iPad, and Mac with single purchase
- OmniQuant and GPTQ quantization for superior output quality
- Supports DeepSeek R1, Llama 3.3 70B, Qwen3, Gemma 3 locally
- 8K token context on iOS, 32K token context on Mac
- Siri and Apple Shortcuts integration without code required
- macOS right-click AI writing tools in any application
- Uncensored model variants available (Abliterated versions)
- Built by bootstrapped engineers with no VC funding pressure
- No restrictions on commercial or production use
- Faster inference than competition on same hardware
- Advanced features like attention sinks and sliding window attention
- Models optimized with Metal kernels for Apple hardware
- Specialized models for coding, biomedical, role-play, multilingual use
- Android beta available as direct APK download
- GDPR compliant with highest privacy standards
PrivateLLM cons
- Only available on Apple devices (iPhone, iPad, Mac) primarily
- Android version is early beta with stability issues
- No document/file reading (RAG) support currently
- Requires significant RAM for larger models
- Older devices (pre-iPhone 12) have limited performance
- Model downloads depend on Hugging Face availability
- Background processing restricted on iOS due to Apple policies
- Hallucinations still possible with long textual content
- No free tier - requires one-time purchase
- Cannot access real-time data or internet information
Frequently asked questions about PrivateLLM
What is Private LLM and how does it work?
Private LLM is your private AI chatbot designed for privacy, convenience, and creativity. It operates entirely offline on your iPhone, iPad, and Mac, ensuring your data stays secure and confidential. The app runs open-source large language models directly on your device using advanced quantization techniques. After a one-time purchase on the App Store, you get unlimited access without any subscription fees. No account, no tracking, no logs, and no internet required after the initial model download.
What makes Private LLM different from other local AI apps like Ollama or LM Studio?
Private LLM stands out through advanced model quantization techniques like OmniQuant and GPTQ, which are optimization-based methods that maintain weight distribution integrity better than the naive Round-To-Nearest (RTN) quantization used by competing apps. It achieves superior model performance and accuracy, nearly matching un-quantized fp16 models with significantly reduced computational requirements. Unlike competitors based on llama.cpp or MLX, Private LLM supports advanced features like attention sinks and sliding window attention, and is significantly faster on the same hardware. The developers carefully tune quantization for each model, not quantizing embeddings and gate layers when it hurts perplexity.
What devices are compatible with Private LLM and what are the RAM requirements?
On iPhones, older devices like iPhone SE 2nd Gen (3GB RAM) can run small models like Llama 3.2 1B and Qwen 2.5 0.5B/1.5B. iPhone 12 (4GB RAM) performs better with 3B models. iPhone 15 Pro or newer with 8GB RAM is recommended for larger models like Llama 3.1 8B or Qwen 2.5 7B. For iPads, devices with at least 4GB RAM run mid-range models, while iPad Pro with 16GB RAM supports Qwen 2.5 14B or Gemma 2 9B. On Macs, Apple Silicon with 8GB RAM runs 8B models, 16GB RAM handles 14B models, 32GB RAM supports Phi-4 and Qwen 2.5 32B, and 48GB+ RAM delivers optimal performance with Llama 3.3 70B.
Does Private LLM collect any of my data or require internet access?
Absolutely not. Private LLM is dedicated to ensuring your privacy, operating solely offline without any internet access for its functions. An internet connection is only required when downloading updates or new models, during which no personal data is collected or transmitted. The app processes everything locally on your device with zero data collection or tracking. Users can verify this using network monitoring tools like Little Snitch. The app aligns with Apple's stringent privacy and security guidelines and upholds the highest data protection standards.
What language models does Private LLM support and which languages are covered?
Private LLM offers models from Llama 3, Qwen 2.5, and Gemma 3 families supporting multiple languages. Llama 3 is proficient in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Qwen 2.5 extends support to over 29 languages including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic. Gemma 3 supports more than 140 languages worldwide. Specialized models include SauerkrautLM Gemma-2 2B IT for German, DictaLM 2.0 Instruct for Hebrew, RakutenAI 7B Chat for Japanese, and Yi 6B/34B Chat for Chinese.
How does Private LLM ensure superior text generation quality?
Private LLM uses advanced quantization strategies like OmniQuant and GPTQ, which take numerous hours to carefully quantize each model on GPUs. This meticulous process preserves the model's weight distribution more effectively, resulting in faster inference, improved model fidelity, and higher-quality text generation. The 3-bit OmniQuant models outperform or match 4-bit RTN-quantized models used by other platforms. Models are quantized in-house (not using readily available GGUF files from Hugging Face), ensuring optimization for speed, accuracy, and quality. This rigorous approach is why Private LLM is a paid app with much better quality than slower, less capable free alternatives.
Can I request specific models to be added to Private LLM?
Yes. New models are regularly added based on user feedback, as shown in the release notes. To request a specific model, join the Discord community and share your suggestion in the #suggestions channel. The developers review all requests and prioritize popular ones for future updates. The model library is constantly expanding with user input driving development priorities.
Does Private LLM support document reading or Retrieval-Augmented Generation (RAG)?
Private LLM does not currently support reading documents or files (RAG functionality). While the app provides impressive context lengths (8K tokens on iPhone/iPad, 32K tokens on Mac), all current local AI implementations face challenges with hallucinations when processing long textual content. Server-based solutions currently offer more reliable results for RAG as they handle larger context lengths better. Document-reading capabilities are planned for future introduction, but for now, server-based solutions are recommended for RAG tasks.
Can Private LLM integrate with Siri and Apple Shortcuts?
Yes. Private LLM has two app intents for use with Siri and the Shortcuts app. You can find Private LLM in the Shortcuts app and build AI-driven workflows without code. The app also supports the x-callback-url specification, allowing integration with 70+ apps. Users can create shortcuts to summarise text, generate writing, or pipe responses into other applications. This enables creative workarounds for incorporating current data (from RSS feeds, web pages, Calendar, Reminders, Notes) while maintaining the offline privacy-first ethos.
What is the refund policy for Private LLM?
If you're considering a refund, you can request one through the Apple App Store. Navigate to your Apple account's purchase history, find Private LLM, and click on 'Report a Problem' to initiate the refund process. The developers would also appreciate feedback on how to improve, so you can reach out via the contact form on their website or join the Discord community to share your thoughts and get support from other users.