Lion Cipher Lab
Run bigger LLMs on a modest CPU with quantized GGUF models
- AI Security
- Intermediate
- ~45 min
- Free · no account required
Why it matters
The open-weight models worth running are often far larger than a normal laptop's memory can hold at full precision, and that wall is where most people give up on local AI and hand their data back to a hosted service. Quantization is the single technique that changes that math: by storing each weight in fewer bits, a model that would need 16 GB can fit in 4 and run on a plain CPU, at the cost of a small, measurable drop in quality. Understanding it is what lets you keep sensitive work local instead of shipping it to someone else's servers, choose the right size/quality point on purpose instead…
What you'll learn
- quantization concepts
- reading GGUF quant levels (Q4_K_M etc.)
- downloading GGUF models from Hugging Face
- running local models with llama.cpp
- running GGUF models with Ollama
- measuring memory/speed vs quality tradeoffs
Steps in this lab
- The problem, and the one idea that solves it
- What GGUF is, and why llama.cpp and Ollama both speak it
- Decoding the quant levels (what Q4_K_M actually means)
- Download a ready-made GGUF from Hugging Face
- Run it with llama.cpp
- Or run the same GGUF with Ollama
- Measure the tradeoff yourself: memory, speed, quality
- The honest tradeoff, and how to think about it