How to run LLMs on PC at home using Llama.cpp

"Hands on Training large language models (LLMs) may require millions or even billion of dollars of infrastructure, but the fruits of that labor are often more accessible than you might think. Many recent releases, including Alibaba's Qwen 3 and OpenAI's gpt-oss, can run on even modest PC hardware. If you really want to learn about how LLMs work, running one locally is essential. It also gives you unlimited access to a chatbot, without paying extra for priority access or sending your data to the cloud."

"While these niceties make running local models less daunting for newcomers, they often leave something to be desired with regard to performance and features. As of this writing, Ollama still doesn't support Llama.cpp's Vulkan back end, which offers broader compatibility and often higher generation performance, particularly for AMD GPUs and APUs. And while LM Studio does support Vulkan, it lacks support for Intel's SYCL runtime and GGUF model creation."

Large language model training can cost millions, but many recent models can run on modest PCs. Running an LLM locally enables unrestricted chatbot access and keeps data off the cloud. Activating Llama.cpp at the command line delivers strong performance, options to assign CPU or GPU workload, and model quantization for faster output. Popular front ends like Ollama, Jan, and LM Studio are wrappers built on Llama.cpp that simplify use but can miss advanced features. Llama.cpp supports Vulkan and tool calling; recommended prerequisites include about 16GB RAM and, optionally, a dedicated GPU for better performance.

Read at Theregister