by Amit Shekhar · 16 July 2026
How does llama.cpp run LLMs on everyday hardware?
How llama.cpp runs large language models on everyday hardware. We will also see what llama.cpp is, why it was created, how it shrinks huge models with quantization, how it loads them quickly, and how it shares work between the CPU and the GPU.
Read on Outcome School ↗then come back to lock it in
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What this article covers
- What is llama.cpp
- Why we needed llama.cpp
- A quick refresher on what an LLM is
- The real problem: models are too big to fit
- The first big idea: quantization
- Understanding names like Q4_K_M
- The GGUF file: everything packed in one box
- Memory mapping: loading the model the smart way
- Squeezing speed out of the CPU
- Sharing the work with the GPU
- The full journey of running a prompt
- Where llama.cpp is used
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