We'reMakingAIPersonal,Private,andPowerful
MyLLM AI isn't just another chatbot app. It's a fundamental rethinking of how AI should work — running entirely on your device, respecting your privacy by design, and giving you access to cutting-edge language models without spending a single rupee.
On-Device Processing
Data Sent to Cloud
Total App Size
Model Families Supported
Cloud AI Has a Trust Problem
Every time you ask ChatGPT a personal question, write code with Copilot, or brainstorm with Claude — your data travels to servers you don't control. Companies promise “we don't train on your data,” but terms change, breaches happen, and regulations vary by country.
Meanwhile, you pay $20-200/month for the privilege of giving away your most intimate thoughts, creative work, and sensitive code to corporations that treat your data as an asset.
We asked a simple question: what if the AI just lived on your phone?
Where Your Data Goes
Cloud AI
Your data crosses multiple boundaries you don't control
MyLLM AI
Everything stays right here. That's it.
What drives every line of code we write
These aren't marketing slogans. They're engineering constraints we build around.
Privacy is Non-Negotiable
Every byte of your data stays on your device. MyLLM has zero network calls for inference — we didn't just minimize data collection, we eliminated it entirely. No analytics SDK, no crash reporters, no telemetry. Your conversations are yours alone.
AI for Everyone, Everywhere
Whether you're on a flight to Tokyo, in a rural village without cell service, or simply value your privacy — MyLLM works. No subscription fees, no internet dependency. Just download once and you have a powerful AI assistant forever.
Open Source to the Core
MyLLM is built entirely on open-source foundations. We use llama.cpp for inference, GGUF for model formats, and publish our integration work. We believe transparency isn't optional — it's the only way to build trust in AI.
Crafted with Obsession
Every interaction is designed to feel native, fast, and delightful. From the smooth chat animations to the one-tap model switching — we obsess over the details because you deserve software that respects your time and intelligence.
Four zeros that define us
Other apps talk about privacy. We engineered it into our architecture so it's physically impossible to violate.
Zero Servers
We don't run inference servers. There's nothing to hack because there's nothing to host.
Zero Tracking
No Google Analytics, no Mixpanel, no Firebase Analytics. We literally don't know how many users we have.
Zero Network Calls
The inference engine has no networking code. It physically cannot send your data anywhere.
Zero Accounts
No sign-up, no login, no email collection. Install and start chatting — that's it.
Engineering that makes magic happen
A carefully architected Android app that bridges Kotlin, C++, and machine learning.
System Architecture
How MyLLM processes your messages — from tap to token
Presentation Layer
Jetpack Compose
Domain Layer
Clean Architecture
Data Layer
Local Persistence
Native Layer
llama.cpp via JNI
Kotlin 2.x
Modern Android with Jetpack Compose, coroutines, and type-safe navigation
llama.cpp
Georgi Gerganov's C++ inference engine — the gold standard for local LLM inference
GGUF Quantization
Q4_K_M, Q5_K_M, Q8_0 — optimized model formats that balance quality and performance
Multi-Module Gradle
Clean separation: app, llm, core/*, feature/* — fast builds, clear boundaries
JNI Bridge
Kotlin ↔ C++ bridge for native inference. Zero-copy token streaming to UI
Hilt + Room + DataStore
Battle-tested Android libraries for DI, database, and preferences
From your question to an AI response
What happens inside MyLLM when you press send — in real time, on your hardware.
You type a message
Your text is formatted into ChatML template — the standard format understood by Qwen, Llama, and other models.
Tokenization
The message is converted into tokens (numeric representations) using the model's built-in tokenizer — all happening in C++ via JNI.
Inference
llama.cpp runs the tokens through the neural network on your CPU/GPU. Each layer of the model processes the input to understand context and meaning.
Token generation
The model generates response tokens one by one, each one streamed instantly to the UI. You see the response appear word by word — just like ChatGPT, but from your phone's processor.
Display & Store
The complete response is rendered in Markdown and saved to your local Room database. Your conversation history never touches a server.
Where we're heading next
Our vision for bringing local AI to every device, every platform, every person.
Q1 2026
Foundation Launch
- Core chat engine with ChatML prompt format
- Model download manager with HuggingFace integration
- Qwen 3.5 series support (0.6B → 8B)
- Basic conversation history with Room DB
Q2 2026
Agent & Intelligence
- Autonomous agent with multi-step reasoning
- 20+ built-in tools (code interpreter, search, files)
- Voice input with on-device speech recognition
- Llama 3.2, Gemma 2, Phi-3.5, SmolLM2, DeepSeek R1 support
Q3 2026
Scale & Distribution
- Google Play Store release
- Community model sharing & custom GGUF imports
- Plugin API v1 for third-party tool developers
- Performance benchmarking dashboard
Q4 2026
Multi-Platform Ecosystem
- iOS app development with Swift + llama.cpp
- Desktop companion app (Windows/Mac/Linux)
- Multi-modal support (image understanding)
- Model fine-tuning toolkit for advanced users
Built on the shoulders of giants
MyLLM wouldn't exist without the incredible open-source community. Here are the projects that power us.
Ready to take AI off the cloud?
Download MyLLM AI and experience what local AI feels like. No sign-up, no credit card, no catch.