Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernels
Introduction
Overview
Explore llama.cpp architecture and the inference workflow
Integrate Streamline Annotations into llama.cpp
Analyze token generation performance with Streamline profiling
Implement operator-level performance analysis with channel annotations
Examine multi-threaded performance patterns in llama.cpp
Next Steps
Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernels
Introduction
Overview
Explore llama.cpp architecture and the inference workflow
Integrate Streamline Annotations into llama.cpp
Analyze token generation performance with Streamline profiling
Implement operator-level performance analysis with channel annotations
Examine multi-threaded performance patterns in llama.cpp
Next Steps
Who is this for?
This is an advanced topic for software developers, performance engineers, and AI practitioners who want to optimize llama.cpp performance on Arm-based CPUs.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Profile llama.cpp architecture and identify the role of the Prefill and Decode stages.
- Integrate marker annotations into llama.cpp for fine-grained performance insights.
- Capture and interpret profiling data with Streamline.
- Analyze specific operators during token generation using channel annotations.
- Evaluate multi-core and multi-thread execution of llama.cpp on Arm CPUs.
Prerequisites
Before starting, you will need the following:
- Basic understanding of llama.cpp
- Understanding of transformer models
- Knowledge of Arm Streamline usage
- An Arm Neoverse or Cortex-A hardware platform running Linux or Android
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
llama.cpp on Arm CPUs with Arm Streamline, including runs that use KleidiAI LLM kernels. First, you’ll add marker annotations for prefill and decode phases and build llama-cli. Then, you’ll prepare the model files and gator daemon, and capture traces. Finally, you’ll inspect channel annotations and operator timing to distinguish pipeline stages and assess multi-core and multi-thread behavior during token generation.Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
llama_decode() and records the start of a token-generation event. Marker annotations include values such as past and n_eval. If marker annotations don’t appear, rebuild llama.cpp with the annotation changes and run the annotated llama-cli.gator daemon is configured and running on the Arm system. Place the built llama-cli and the required model files on the target so that Streamline can capture a representative run.