Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernels

About this Learning Path

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 Streamline Annotations into llama.cpp for fine-grained performance insights
• Capture and interpret profiling data with Streamline
• Analyze specific operators during token generation using Annotation Channels
• 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