In the rapidly evolving landscape of local AI chatbots for Apple devices, Private LLM and Apollo AI offer distinct approaches to on-device AI interactions. While both emphasize user privacy by processing data locally, they differ significantly in model support, performance, context window capabilities, and data privacy practices.

Side-by-Side Feature Comparison

Feature	Private LLM	Apollo AI
Model Support	Llama 3.3, 3.2, 3.1, Qwen 2.5, Qwen 2.5 Coder, Google Gemma; uncensored and role-play models	Access via OpenRouter; connections to user-hosted models via LM Studio or Ollama
Quantization Techniques	OmniQuant, GPTQ; superior performance and speed	RTN(Round To Nearest); models have higher perplexity and ceteris paribus, are more likely to hallucinate.
Context Window Support	8K tokens (iPhone/iPad), 32K tokens (Mac)	UI displays blatantly false and misleading information about supported context window size.
Device Compatibility	iPhone, iPad, Mac (single purchase)	No Mac app; relies on third-party tools like Ollama and LM Studio, which also use RTN quants
Privacy	Entirely offline; zero data collection or tracking	Uses Sentry for crash logs; may collect non-personal data about app usage and device details

Key Differences

Model Support and Performance

Private LLM supports a wide range of models, including the latest versions of Llama 3.3, Llama 3.2, Llama 3.1, Qwen 2.5, Qwen 2.5 Coder, and Google Gemma. Additionally, it offers numerous uncensored and role-play optimized fine-tunes of these models, providing users with versatile AI interactions. The use of advanced quantization techniques like OmniQuant and GPTQ ensures superior text generation quality and speed.

In contrast, Apollo AI provides access to various models through OpenRouter integration and allows connections to user-hosted models via tools like LM Studio or Ollama. However, OpenRouter is server-based, meaning interactions are not truly private or on-device, and similar functionalities are available in other clients. Furthermore, Apollo AI's reliance on RTN (Round-To-Nearest) quantization may result in reduced performance, especially in demanding applications.

For users seeking to run LLMs on their Macs and seamlessly interact with their iPhones, FOSS apps like Enchanted (GitHub Repository) provide a superior alternative to Apollo AI for that specific use case. Enchanted's open-source approach and better integration make it a compelling choice for those focused on flexibility and functionality within the Apple ecosystem.

Context Window Support

Private LLM offers transparent and substantial context window support, providing 8K tokens on iPhone and iPad, and an impressive 32K tokens on Macs. This capability allows for more detailed and accurate responses, accommodating complex interactions. In contrast, competitors like Ollama typically support a default context length of 2K tokens, while LM Studio defaults to 1.5K tokens. It's important to note that Apollo AI's UI may display misleading information regarding maximum context window support, whereas Private LLM is transparent about its capabilities.

Integration with Apple Ecosystem

Private LLM is available across iPhone, iPad, and Mac with a single purchase, providing seamless integration within the Apple ecosystem. This cross-device compatibility enhances user experience and accessibility. In contrast, Apollo AI does not offer a Mac app and relies heavily on third-party tools like Ollama and LM Studio for backend support, which may limit its utility for users seeking a cohesive Apple experience.

Privacy and Data Collection

Private LLM operates entirely offline, ensuring that all data remains on your device with zero data collection or tracking. This commitment to privacy is particularly noteworthy, especially given the challenges faced by VC-backed companies under pressure to find scalable revenue streams.

In contrast, Apollo AI's privacy practices fall short of this standard. According to its privacy policy, Apollo uses Sentry to collect crash and exception logs, which may include non-personal data about app usage and device details. Additionally, Apollo AI leverages Apple's APIs for speech recognition, meaning speech data from the app is sent to Apple to process user requests. This data may also be used by Apple to improve its speech recognition technology. While this enables speech-to-text functionality, it raises concerns about data being processed outside the user's device.

Apollo AI requesting access to speech recognition with a notification explaining that speech data will be sent to Apple to process requests and improve its speech recognition technology — Apollo AI requesting access to speech recognition, highlighting potential privacy concerns as speech data is sent to Apple for processing and improvement of their technology.

Private LLM, on the other hand, does not currently offer speech-to-text support. However, we are actively exploring privacy-friendly ways to implement this feature without compromising user privacy, ensuring that all interactions remain fully local and secure.

Conclusion

While both Private LLM and Apollo AI aim to provide local AI chatbot experiences on Apple devices, Private LLM stands out due to its superior model quantization, a vastly larger set of supported models, performance, context window capabilities, seamless integration with the Apple ecosystem, and robust privacy practices. In contrast, Apollo AI tries to do too many things—connecting to OpenRouter, Ollama, and LM Studio—and ends up doing them all poorly. Its lack of a Mac app and reliance on subpar quantization further limits its appeal.

For users seeking a solution to run LLMs across their Macs and iPhones, the FOSS app Enchanted is a better alternative to Apollo AI for that specific use case. However, for those focused on a fast, private, and high-quality on-device LLM experience, Private LLM remains the gold standard.