As on-device AI tools spread through the Apple ecosystem, choosing the right local AI chatbot can make a big difference. Private LLM and PocketPal AI both run locally on your device, aiming to protect your data. Still, their approaches vary when it comes to performance, user experience, platform availability, and more.

Side-by-Side Feature Comparison

Feature	Private LLM	PocketPal AI
Platforms	iOS, iPadOS, macOS	iOS, Android
Pricing	One-time purchase; Family Sharing	Free, open-source
Technology Stack	Native Swift app with MLC-LLM backend	React Native app with llama.cpp
Quantization	OmniQuant / GPTQ for faster output	RTN quantization (round-to-nearest)
Model Download	Curated based on user requests, quantized by us	User can download models from HuggingFace
Performance	Faster tokens/sec with Game Mode	Slower, may lag on complex prompts
Context Length	Handles longer context length with ease	Struggles with larger context length
Apple Ecosystem Support	Siri & Apple Shortcuts integration	No Siri or Shortcuts support
Chat History	Not yet available (on roadmap)	Has chat history
Privacy Policy	Clear, no data collected	Unclear, limited info provided

Detailed Comparison

Platforms and Integration with Apple Ecosystem

Private LLM supports iOS, iPadOS, and macOS. It’s a native Swift app that runs efficiently across Apple devices. This native approach provides direct access to iOS APIs and features like Game Mode, improving speed and responsiveness. It also integrates with Siri and Apple Shortcuts, letting you build custom workflows without coding.

PocketPal AI supports iOS and Android. Built with React Native, it bridges JavaScript and native components, which can introduce lag. It does not support Apple Shortcuts, and lacks system-level features like Game Mode.

Performance and Quantization

Private LLM uses advanced OmniQuant and GPTQ quantization. This results in higher-quality output and smoother, faster text generation. The native approach ensures lower latency, making it a great choice for users who need speed and accuracy.

PocketPal AI uses RTN quantization and a React Native front end. This can lead to slower inference and occasional lags when handling larger or more complex prompts.

Context Length

When it comes to dealing with longer documents or larger prompts, Private LLM shines. It can summarize lengthy texts and stay on track without losing context, even at around 7K tokens and beyond.

PocketPal AI tends to slow down and struggle with similar tasks. In our tests, it hallucinated and failed to follow instructions on a 7K token prompt. Private LLM handled the same prompt easily, which we’ll showcase in a video.

Model Availability

Private LLM offers a carefully curated selection of models, optimized for performance and quality using advanced quantization on high-end GPUs. Additionally, we actively expand our model library based on user feedback. New models are routinely added, and if you have specific requests, feel free to share them on our Discord community. Popular demands often guide our updates, ensuring the app evolves with user needs.

PocketPal AI lets you download models directly from HuggingFace. While this gives you more choice, the results may vary, and you might not see the same level of speed or output quality.

Chat History

PocketPal AI includes chat history, which can be helpful if you often revisit past conversations. Private LLM doesn’t have chat history yet, but we’re actively working on adding it.

Privacy and Transparency

Both apps prioritize offline operation, meaning your data stays on your device. However, we recommend reviewing the privacy policies of both apps to make an informed decision. Private LLM provides a clear and transparent privacy policy that outlines its compliance with GDPR, CCPA, and LGPD regulations. PocketPal AI’s privacy policy is available here. Take a moment to compare them and decide which aligns better with your expectations.

Putting It to the Test

We’ll share videos comparing the two apps on common tasks. For example, when asked, “A farmer has 17 sheep, and all but 9 run away. How many sheep are left?” both apps use the same Llama 3.2 3B model with similar temperature and sampling settings. Private LLM gets the answer right, while PocketPal AI misses it. You’ll also see how quickly Private LLM processes text compared to PocketPal AI.

Bottom Line

Private LLM is built natively for Apple devices and uses advanced quantization techniques to deliver fast, high-quality responses. It integrates with the Apple ecosystem, handles large context prompts, and keeps privacy at the forefront. The one-time purchase covers all your Apple devices, and if it doesn’t meet your expectations, you can always claim a refund.

PocketPal AI, while free and open-source, runs on iOS and Android, may lag on heavier prompts, and lacks the level of Apple integration and privacy transparency found in Private LLM.

Don’t just take our word for it—benchmark it yourself. Give Private LLM a try, and if you’re not satisfied, you’re welcome to claim a refund. It’s as simple as that.

Ready to experience a fast local AI solution with a focus on privacy and performance? Download Private LLM today!