Llama Cpp
If you're running language models on hardware without NVIDIA GPUs, this is your go-to. It's a pure C/C++ implementation optimized for CPU inference and non-CUDA setups, which means it shines on Apple Silicon, AMD or Intel GPUs, and even edge devices like Raspberry Pis. The appeal is minimal dependencies and straightforward deployment without needing Docker or Python environments. With nearly 28K GitHub stars, llama.cpp has proven itself in production. The skill documentation is clear about the tradeoffs: use this for CPU-first deployments, but if you've got NVIDIA datacenter GPUs and need maximum throughput, you'll want TensorRT-LLM instead.
- skills.sh installs
- 475
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill llama-cpp --agent claude-codeInstalls into .claude/skills of the current project.
Files
If you're running language models on hardware without NVIDIA GPUs, this is your go-to. It's a pure C/C++ implementation optimized for CPU inference and non-CUDA setups, which means it shines on Apple Silicon, AMD or Intel GPUs, and even edge devices like Raspberry Pis. The appeal is minimal dependencies and straightforward deployment without needing Docker or Python environments. With nearly 28K GitHub stars, llama.cpp has proven itself in production. The skill documentation is clear about the tradeoffs: use this for CPU-first deployments, but if you've got NVIDIA datacenter GPUs and need maximum throughput, you'll want TensorRT-LLM instead.
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- Repository
- github.com/davila7/claude-code-templates
- Categories
- Backend & APIs, AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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