llama.cpp Windows builds favor CUDA and GGUF

Hot take: this is one of the least painful ways to run local models on Windows now, especially if you lean on the prebuilt CUDA binaries instead of compiling from source.

• –Official docs say llama.cpp can be installed with winget, via prebuilt Windows release zips, or by building from source.
• –The model must be in GGUF format, so the real workflow is “get a GGUF model, then run it locally.”
• –On NVIDIA hardware, the project supports CUDA and CPU+GPU hybrid inference, which is a good fit for an A4000 16 GB when you want to offload as much as VRAM allows.
• –The simplest commands are llama-cli -m my_model.gguf for direct use and llama-server -m model.gguf --port 8080 for a local API/UI.
• –For Windows releases, the repo now publishes x64 CPU, CUDA 12, CUDA 13, Vulkan, SYCL, and HIP builds, so you can pick the backend that matches your setup.