# Next Steps

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/)
- [Overview](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/1_overview/)
- [Explore llama.cpp architecture and the inference workflow](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/2_llama.cpp_intro/)
- [Integrate Streamline Annotations into llama.cpp](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/3_llama.cpp_annotation/)
- [Analyze token generation performance with Streamline profiling](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/4_analyze_token_prefill_decode/)
- [Implement operator-level performance analysis with Annotation Channels](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/5_operator_deepdive/)
- [Examine multi-threaded performance patterns in llama.cpp](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/6_multithread_analyze/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_next-steps/)

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## Continue Learning

### Read related resources
Find more information about the topics in this Learning Path:
- [llama.cpp project](https://github.com/ggml-org/llama.cpp)
- [Build and run llama.cpp on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama-cpu/)
- [Run a Large Language Model chatbot with PyTorch using KleidiAI](https://learn.arm.com/learning-paths/servers-and-cloud-computing/pytorch-llama/)
- [Arm Streamline User Guide](https://developer.arm.com/documentation/101816/9-7)
- [KleidiAI project](https://github.com/ARM-software/kleidiai)

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