Running large language models directly on mobile devices is one of the most exciting frontiers in local AI, enabling truly private, offline AI assistants that work without cloud connectivity. Llamafu and MLC LLM are two frameworks tackling this challenge from different angles — Llamafu brings llama.cpp to Flutter for cross-platform mobile development, while MLC LLM uses machine learning compilation to optimize models for diverse hardware targets including phones, tablets, and browsers. This comparison helps mobile developers choose the right framework for shipping on-device AI features.
Quick Comparison
| Feature | Llamafu | MLC LLM |
|---|---|---|
| Developer | Community (Flutter + llama.cpp) | MLC AI (CMU origin) |
| Approach | llama.cpp FFI bindings for Flutter | ML compiler (TVM-based) optimized inference |
| Primary language | Dart (Flutter) | Swift, Kotlin, JavaScript, Python, Rust |
| Mobile platforms | iOS, Android (via Flutter) | iOS, Android (native) |
| Desktop platforms | macOS, Windows, Linux (via Flutter) | macOS, Windows, Linux |
| Web/browser | Flutter web (limited) | WebGPU, WebAssembly |
| Model format | GGUF | MLC-compiled models |
| Model preparation | Download GGUF, use directly | Compile model for target platform |
| Underlying engine | llama.cpp | TVM runtime |
| GPU on mobile | Metal (iOS), Vulkan/OpenCL (Android) | Metal (iOS), Vulkan/OpenCL (Android), WebGPU |
| NPU support | Limited | Experimental (QNN, CoreML) |
| Quantization | GGUF quantizations (Q4, Q5, Q8, etc.) | INT4, INT8, FP16 (per target) |
| Chat UI | Build in Flutter | Reference apps provided |
| License | MIT | Apache 2.0 |
| Community size | Smaller (Flutter niche) | Larger (multi-platform) |
Flutter Integration
Llamafu
Llamafu is built for Flutter developers. It provides Dart bindings to llama.cpp through FFI (Foreign Function Interface), allowing Flutter apps to run LLM inference natively on iOS and Android from a single Dart codebase.
The integration follows Flutter conventions:
- Dart API: Load models, manage conversations, and stream tokens using idiomatic Dart code
- Widget-friendly: Streaming responses work naturally with Flutter’s
StreamBuilderwidget for real-time UI updates - Asset management: Models can be bundled as app assets or downloaded at runtime
- Cross-platform: The same Dart code runs on iOS, Android, macOS, Windows, and Linux through Flutter’s platform channels
For Flutter developers, Llamafu provides the path of least resistance to adding on-device AI. You stay in the Dart ecosystem, use familiar Flutter patterns, and get cross-platform inference without writing platform-specific code.
The tradeoff is that Llamafu ties you to Flutter. If your app is built with Swift (iOS) or Kotlin (Android), Llamafu is not the right choice.
MLC LLM
MLC LLM does not have Flutter-specific bindings. Instead, it provides native SDKs for each platform:
- Swift package for iOS and macOS
- Kotlin/Java library for Android
- JavaScript/TypeScript for web (via WebGPU/WebAssembly)
- Python for desktop and server
- Rust bindings for systems programming
For Flutter developers, using MLC LLM requires writing platform channels — Dart code calls Swift on iOS and Kotlin on Android, adding complexity. However, for native mobile developers, MLC LLM’s platform-native SDKs feel more natural than FFI bindings.
MLC LLM also provides reference chat applications for iOS and Android that demonstrate on-device inference. These apps can serve as starting points or production-ready interfaces.
Platform Support
Llamafu
Llamafu’s platform support mirrors Flutter’s platform support:
| Platform | Status | GPU Backend |
|---|---|---|
| iOS (14+) | Supported | Metal |
| Android (API 24+) | Supported | Vulkan, OpenCL |
| macOS | Supported | Metal |
| Windows | Supported | CUDA, Vulkan |
| Linux | Supported | CUDA, Vulkan |
| Web | Experimental | Limited |
The key advantage is a single codebase across all platforms. Build once in Dart, deploy everywhere Flutter runs. The GPU backends are inherited from llama.cpp, which has mature Metal and Vulkan support.
MLC LLM
MLC LLM’s platform support is broader and more optimized per-platform:
| Platform | Status | GPU Backend |
|---|---|---|
| iOS (15+) | Supported | Metal |
| Android (API 26+) | Supported | Vulkan, OpenCL |
| macOS | Supported | Metal |
| Windows | Supported | CUDA, Vulkan |
| Linux | Supported | CUDA, Vulkan, ROCm |
| Web (Chrome) | Supported | WebGPU |
| Web (all) | Supported | WebAssembly (CPU) |
MLC LLM’s WebGPU support is a notable differentiator — it enables LLM inference directly in the browser with GPU acceleration. This opens use cases like browser-based AI assistants that run entirely client-side, with no server required.
MLC LLM also has experimental support for neural processing units (NPUs) through Qualcomm’s QNN and Apple’s CoreML backends, which can provide better power efficiency than GPU inference on supported devices.
Model Compatibility
Llamafu
Llamafu uses GGUF models, which means any GGUF model that llama.cpp supports works with Llamafu. This includes:
- Llama (1, 2, 3, 3.1, 3.2), Mistral, Mixtral, Phi (2, 3, 3.5, 4), Gemma, Qwen, and many more
- All GGUF quantization levels (Q2 through Q8, IQ formats)
- Models from the vast GGUF ecosystem on Hugging Face
The advantage is zero model preparation — download a GGUF file and load it. No compilation, no conversion, no per-platform optimization. This simplicity is valuable for rapid prototyping and for apps that let users bring their own models.
The disadvantage is that GGUF models are not specifically optimized for each target platform. A GGUF model runs on any platform llama.cpp supports, but it may not take full advantage of platform-specific hardware features.
MLC LLM
MLC LLM requires a model compilation step that converts models from Hugging Face format into platform-optimized binaries. The MLC compilation process:
- Takes a Hugging Face model (or pre-quantized model)
- Applies quantization (INT4, INT8) if needed
- Compiles an optimized runtime for the target platform (iOS Metal, Android Vulkan, WebGPU, etc.)
- Produces a platform-specific model package
This compilation step takes time (10-60 minutes depending on model size) and must be done for each target platform. However, the resulting models are optimized for the specific hardware — Metal shaders for Apple devices, Vulkan compute for Android, WebGPU shaders for browsers.
MLC LLM supports major architectures: Llama, Mistral, Phi, Gemma, GPT-2, GPT-NeoX, and others. The MLC team maintains a repository of pre-compiled models for popular architectures, reducing the need for users to compile themselves.
The tradeoff: MLC LLM models are more work to prepare but potentially faster on the target device.
Performance
Mobile Inference Speed (Approximate tok/s for Llama 3.2 3B, 4-bit)
| Device | Llamafu | MLC LLM |
|---|---|---|
| iPhone 15 Pro (A17 Pro) | ~18 | ~22 |
| iPhone 14 Pro (A16) | ~14 | ~17 |
| Samsung S24 Ultra (Snapdragon 8 Gen 3) | ~12 | ~16 |
| Pixel 8 Pro (Tensor G3) | ~9 | ~12 |
| iPad Pro M4 | ~30 | ~35 |
MLC LLM generally achieves 15-30% faster inference on mobile devices due to its platform-specific compilation optimizations. The TVM compiler generates hardware-optimized kernels that outperform llama.cpp’s more general-purpose approach on specific targets.
However, the gap narrows with each llama.cpp release. llama.cpp’s Metal and Vulkan backends are continually improving, and the performance difference may not justify MLC LLM’s additional compilation complexity for many use cases.
Memory Usage
Both frameworks face the same fundamental constraint: mobile devices have limited RAM, and the operating system will kill apps that use too much memory.
| Model Size (4-bit) | Approximate RAM Required | Practical Minimum Device RAM |
|---|---|---|
| 1B parameters | ~0.8 GB | 4 GB |
| 3B parameters | ~2.0 GB | 6 GB |
| 7B parameters | ~4.5 GB | 12 GB |
| 13B parameters | ~8.0 GB | Not practical on most phones |
MLC LLM’s compiled models are sometimes slightly more memory-efficient because the compiler can optimize memory layout for the target platform. Llamafu uses llama.cpp’s standard memory management, which is efficient but not platform-specialized.
In practice, the memory difference between the two frameworks is small (5-10%). The model size and quantization level are the dominant factors.
Features
Llamafu
- Streaming generation: Token-by-token streaming with Dart streams
- Conversation management: Multi-turn conversation with context
- Model hot-swapping: Load and unload models dynamically
- Background inference: Run inference in an isolate to keep UI responsive
- GGUF flexibility: Use any GGUF quantization level
- Embeddings: Generate embeddings for on-device semantic search
- Grammar-constrained generation: JSON mode and structured output
MLC LLM
- Streaming generation: Token-by-token streaming on all platforms
- Chat completions API: OpenAI-compatible API for local serving
- WebGPU inference: Browser-based inference without server
- NPU exploration: Experimental hardware accelerator support
- Pre-built chat apps: Ready-to-use iOS and Android chat applications
- Multi-model support: Load different models for different tasks
- Speculative decoding: Faster generation with draft models (on some platforms)
- Structured generation: JSON schema-constrained output
MLC LLM has a broader feature set overall, particularly with its WebGPU support and reference applications. Llamafu’s feature set is more focused but integrates more naturally into Flutter’s widget and state management patterns.
Developer Experience
Llamafu
For Flutter developers, Llamafu provides a familiar development experience. You add a package dependency, import the library, and call Dart methods. Hot reload works for UI changes (though model reloading is slow). The Flutter DevTools can be used for profiling performance.
The challenge is debugging native code issues. When something goes wrong at the llama.cpp level (memory allocation failures, model loading errors), the error messages may not be Dart-friendly, and debugging requires understanding both the Dart and native layers.
MLC LLM
MLC LLM requires more setup but provides a more transparent development experience. The model compilation step is explicit — you see exactly what optimizations are applied. The platform-native SDKs use each platform’s standard development tools (Xcode for iOS, Android Studio for Android), which means platform-specific debugging tools work natively.
The challenge is the compilation workflow. Changing quantization, updating a model, or targeting a new platform requires re-running the compilation pipeline, which adds friction to the development cycle.
The Bottom Line
Choose Llamafu if you are a Flutter developer building a cross-platform app with on-device AI. Its Dart bindings, Flutter widget compatibility, and single-codebase approach make it the fastest path to shipping AI features in a Flutter app. The ability to use GGUF models without compilation reduces friction for rapid development.
Choose MLC LLM if you are building native mobile apps (Swift/Kotlin), need browser-based inference via WebGPU, or want maximum inference performance on specific hardware targets. Its platform-specific compilation produces faster models, and its broader SDK coverage (Swift, Kotlin, JavaScript, Rust) fits non-Flutter development workflows.
For mobile AI in general, both frameworks demonstrate that useful LLM inference on smartphones is practical today with 3B models, and feasible with 7B models on flagship devices. The mobile AI space is evolving rapidly, and both projects are actively improving performance and expanding model support. Your choice should be primarily driven by your development framework (Flutter vs native) rather than by inference performance, which is competitive between the two.