LLM Model Names Decoded: A Developer's Guide to Parameters, Quantization & Formats

TL;DR: "B" = billions of parameters. "IT" = instruction tuned. "Q4_K_M" = 4-bit quantization, a common default. "GGUF" = the format for Ollama and local tools. "MoE" = only a fraction of parameters activate per token. This guide decodes every component of LLM model names, explains quantization formats and file types, and points you to the best resources for researching which model fits your hardware and use case. If you've ever stared at a Hugging Face model page and seen something like unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF and wondered what any of that means — this guide is for you. The open-weight model ecosystem has exploded. Gemma 4, Qwen 3.5, Llama 4, DeepSeek, Mistral — every family ships dozens of variants across different sizes, architectures, quantization levels, and file formats. Picking the right one for your hardware and use case shouldn't require a PhD. I wrote this as a companion to my local LLM inference tools guide , which covers how to run models. This guide explai