# [Measure LLM inference performance with KleidiAI and SME2 on Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/)
- [Understand how SME2 and KleidiAI accelerate LLM inference in llama.cpp](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/introduction/)
- [Trace how KleidiAI and SME2 accelerate llama.cpp from model load to token decode](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/kleidiai_integration/)
- [Build llama.cpp with KleidiAI and SME2 enabled](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/build_llama_cpp/)
- [Measure SME2 acceleration in llama.cpp on Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/run_llm/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/performance_llama_cpp_sme2/_next-steps/)

## About this Learning Path

| Skill level:            | Advanced         |
|-------------------------|------------------|
| Reading time:           | 40 min           |
| Last updated:           | 19 Aug 2026      |

| Author:                | Zenon Zhilong Xiu, Arm [LinkedIn](https://linkedin.com/in/zenon-zhilong-xiu-491bb398)  |
|------------------------|---------------------------------------------------|
| Arm IP:                | [Arm C1](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors)  |
| Tags:                  | ML, Android, Linux, SME2, CPP, llama.cpp          |

### Who is this for?
This is an advanced topic for software developers, performance engineers, and AI practitioners.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Build llama.cpp with KleidiAI and SME2 support
- Profile large language model (LLM) inference performance on Android
- Understand how KleidiAI and SME2 accelerate LLM operators

### Prerequisites
Before starting, you will need the following:
- Knowledge of KleidiAI and SME2
- A Linux host machine (x86_64 or aarch64) for building `llama.cpp` with the Arm GNU Toolchain
- Git, CMake, and Android Debug Bridge (`adb`) installed on your host machine
- An Android device with Arm SME2 support for running and profiling the executable

### Summary
You’ll build `llama.cpp` with KleidiAI and SME2, then measure LLM inference on Android. First, you’ll cross-compile `llama-cli` for `aarch64`, deploy it with `adb`, and run the Llama-3.2-3B-Instruct-Q4_0 model with SME2 enabled and disabled. Then, you’ll trace acceleration through `ggml-cpu` into KleidiAI, and verify that SME2 microkernels are active before comparing the reported performance.

### Frequently asked questions
#### Which compiler and toolchain should I use to build with SME2?
Use the `aarch64` GCC cross-compile toolchain with the `aarch64-none-linux-gnu-` prefix from the Linux-hosted Arm GNU Toolchain. You need GCC version 14.2 or later to enable SME2.

#### Why do I need a Linux host to build llama.cpp?
The build uses the Linux-hosted Arm GNU Toolchain. If you work on macOS or Windows, run the commands in a Linux virtual machine, container, or development machine.

#### What files must be on the Android device before measuring performance?
Place the built `llama-cli` executable and the `Llama-3.2-3B-Instruct-Q4_0.gguf` model on the device.

#### How do I verify that the SME2 path is used during inference?
Follow the verification step that confirms SME2 microkernels are active. The `ggml-cpu` backend selects KleidiAI SME2 microkernels when the hardware supports them.

#### How should I compare runs with SME2 enabled and disabled?
Run the model on the Android device twice — once with SME2 enabled and once with it disabled. Compare the performance reported by `llama.cpp` from both runs.