# Next Steps

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/)
- [Explore Grace Blackwell architecture for efficient quantized LLM inference](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/1_gb10_introduction/)
- [Verify your Grace Blackwell system readiness for AI inference](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/1a_gb10_setup/)
- [Build the GPU version of llama.cpp on GB10](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/2_gb10_llamacpp_gpu/)
- [Build the CPU version of llama.cpp on GB10](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/3_gb10_llamacpp_cpu/)
- [Analyze CPU instruction mix using Process Watch](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/4_gb10_processwatch/)
- [Next Steps](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_next-steps/)

## Continue Learning

### Read related resources
Find more information about the topics in this Learning Path:
- [NVIDIA DGX Spark website](https://www.nvidia.com/en-gb/products/workstations/dgx-spark/)
- [NVIDIA DGX Spark Playbooks GitHub repository](https://github.com/NVIDIA/dgx-spark-playbooks)
- [Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernels Learning Path](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/)
- [Arm-Powered NVIDIA DGX Spark Workstations to Redefine AI](https://newsroom.arm.com/blog/arm-powered-nvidia-dgx-spark-ai-workstations)

### Join the Arm Developer Program
Connect, upskill, and build with the Arm Developer Community. Join today for hands-on technical resources and education materials, along with the support of Arm engineers and the broader ecosystem. 

### Back to Learning Paths
[Back to all learning paths under Laptops and Desktops](https://learn.arm.com/learning-paths/laptops-and-desktops/)