Self-hosted LLMs come with a lot of vocabulary. Most of it sounds harder than it is because the words arrive before the mental picture.
Once the core terms click, the whole stack becomes easier to navigate.
The Model Words
Parameters are the learned weights inside the model. More parameters can mean more capability, but also more memory. Instruct usually means the model has been tuned to follow directions. Base means it is closer to the raw pretrained model and usually less friendly for chat.
The Memory Words
Context window is how much text the model can consider at once. Tokens are the pieces of text the model reads and writes. VRAM is GPU memory, and it becomes one of the main limits when running models locally.
Tokens are the meter. Context is the room. VRAM is the floor space.
The File Words
GGUF
A local-friendly model file format commonly used with llama.cpp-based tools.
Quantization
A way to shrink model weights so larger models can fit on smaller hardware.
The System Words
Runtime is the software that runs the model. Embedding is a numeric representation of meaning. RAG is the pattern where your documents are searched first, then relevant pieces are sent to the model.
You do not need to master the whole vocabulary at once. Learn the terms that explain the next problem in front of you.