AppRecs review analysis
AppRecs rating 4.3. Trustworthiness 89 out of 100. Review manipulation risk 19 out of 100. Based on a review sample analyzed.
★★★★☆
4.3
AppRecs Rating
Ratings breakdown
5 star
83%
4 star
0%
3 star
0%
2 star
0%
1 star
17%
What to know
✓
Low review manipulation risk
19% review manipulation risk
✓
Credible reviews
89% trustworthiness score from analyzed reviews
✓
High user satisfaction
83% of sampled ratings are 4+ stars (4.3★ average)
About Local LLM: MITHRIL
Access state-of-the-art quantized AI models optimized for mobile hardware. Download GGUF-format models that compress billion-parameter networks into mobile-friendly sizes while maintaining performance.
COMPLETE MODEL SUITE
• Llama 3.2 1B/3B (Meta) - Q4/Q8 quantization
• Gemma 3 270M/2B/9B (Google) - IQ4_NL optimization
• Qwen 2.5 0.5B-7B (Alibaba) - Multiple quantization levels
• LLaVA 1.5/1.6 (Vision) - Multimodal image understanding
• Direct integration with Hugging Face model repository
TECHNICAL FEATURES
• GGML/llama.cpp inference engine
• Metal GPU acceleration on Apple Silicon
• Dynamic context window management (2K-8K tokens)
• Retrieval-Augmented Generation (RAG) with embeddings
• Real-time streaming with token/second metrics
• SQLite conversation storage with vector search
SYSTEM REQUIREMENTS
Models run efficiently when file size ≤ available RAM. Recommended minimum 6GB RAM for larger models. iPhone 15 Pro/Pro Max optimal. iOS26 for Apple foundation model.
Zero telemetry. Zero data transmission. Pure local AI computing.
Tap to Rate:
Reviews for Local LLM: MITHRIL
Costelloem
Best app for local LLMs
Currently the best App Store iOS app for running local LLMs. Despite its many shortcomings it still gets 5 stars simply because there is currently no competition to this app to my knowledge. It is the only app capable of running any model that’s compatible with llama.cpp. This allows the use of custom fine tuned models downloaded from Hugging Face which is essential as 4B parameters is about the maximum that an iPhone can currently handle. It really needs some improvements such as the ability to copy prompts and answers to the system clipboard. Currently the best workaround for anyone interested is to export data (at the bottom of the screen) and parse the export file, Or take a screenshot and use the iOS Live Text feature to copy the text to clipboard although this is impractical for long answers.
the_real_Oo
Perfect for local models
This works better then I would expect on a phone. Would love to be able to host an openai style service on it with it.
