Cloud-based LLMs are powerful, but they’re not always the right tool for mobile apps.

They introduce: • Network dependency • Latency • Usage-based costs • Privacy concerns

As Android developers, we already ship complex logic on-device. So the real question is:

Can we run LLMs fully offline on Android, using Kotlin?

Yes — and it’s surprisingly practical today.

In this article, I’ll show how to run LLMs locally on Android using Kotlin, powered by llama.cpp and a Kotlin-first library called Llamatik.

Why run LLMs offline on Android?

Offline LLMs unlock use cases that cloud APIs struggle with: • 📴 Offline-first apps • 🔐 Privacy-preserving AI • 📱 Predictable performance & cost • ⚡ Tight UI integration

Modern Android devices have: • ARM CPUs with NEON • Plenty of RAM (on mid/high-end devices) • Fast local storage

The challenge isn’t hardware — it’s tooling.

llama.cpp: the engine behind on-device LLMs

llama.cpp is a high-performance C++ runtime designed to run LLMs efficiently on CPUs.

Why it’s ideal for Android: • CPU-first (no GPU required) • Supports quantized GGUF models • Battle-tested across platforms

The downside? It’s C++, and integrating it directly into Android apps is painful.

That’s where Llamatik comes in.

What is Llamatik?

Llamatik is a Kotlin-first library that wraps llama.cpp behind a clean Kotlin API.

It’s designed for: • Android • Kotlin Multiplatform (iOS & Desktop) • Fully offline inference

Key features: • No JNI in your app code • GGUF model support • Streaming & non-streaming generation • Embeddings for offline RAG • Kotlin Multiplatform–friendly API

You write Kotlin — native complexity stays inside the library.

Add Llamatik to your Android project

Llamatik is published on Maven Central.

dependencies {
implementation("com.llamatik:library:0.12.0")
}

No custom Gradle plugins. No manual NDK setup.

Add a GGUF model

Download a quantized GGUF model (Q4 or Q5 recommended) and place it in:

androidMain/assets/
└── phi-2.Q4_0.gguf

Quantized models are essential for mobile performance.

Load the model

val modelPath = LlamaBridge.getModelPath("phi-2.Q4_0.gguf")
LlamaBridge.initGenerateModel(modelPath)

This copies the model from assets and loads it into native memory.

Generate text (fully offline)

val response = LlamaBridge.generate(
"Explain Kotlin Multiplatform in one sentence."
)

No network. No API keys. No cloud calls.

Everything runs on-device.

Streaming generation (for chat UIs)

Streaming is critical for good UX.

LlamaBridge.generateStreamWithContext(
system = "You are a concise assistant.",
context = "",
user = "List three benefits of offline LLMs.",
onDelta = { token ->
// Append token to your UI
},
onDone = { },
onError = { error -> }
)

This works naturally with: • Jetpack Compose • ViewModels • StateFlow

Embeddings & offline RAG

Llamatik also supports embeddings, enabling offline search and RAG use cases.

LlamaBridge.initModel(modelPath)
val embedding = LlamaBridge.embed("On-device AI with Kotlin")

Store embeddings locally and build fully offline AI features.

Performance expectations

On-device LLMs have limits — let’s be honest: • Use small, quantized models • Expect slower responses than cloud GPUs • Manage memory carefully • Always call shutdown() when done

That said, for: • Assistive features • Short prompts • Domain-specific tasks

The performance is absolutely usable on modern devices.

When does this approach make sense?

Llamatik is a great fit when you need: • Offline support • Strong privacy guarantees • Predictable costs • Tight UI integration

It’s not meant to replace large cloud models — it’s edge AI done right.

⸻

Try it yourself

Final thoughts

Running LLMs offline on Android using Kotlin is no longer experimental.

With the right abstractions, Kotlin developers can build private, offline, on-device AI — without touching C++.

If you’re curious about pushing AI closer to the device, this is a great place to start.