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This guide demystifies the nomenclature of local Large Language Models, specifically focusing on how Mixture-of-Experts (MoE) architectures decouple total parameter counts from active inference weights. Understanding these naming conventions is critical for practitioners selecting efficient models that fit specific hardware constraints without overestimating memory requirements.

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A Complete Guide to Decoding LLM Model Names

Analytics VidhyaSeptember 2, 20265 min read

If you have ever tried downloading a local LLM, you have probably seen model names that look like this: Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound At first, it looks like meaningless technical shorthand. It isn’t! Every part of that name tells you something about the model: how large it is, how it is built, how much of it is used at a time, how […] The post A Complete Guide to Decoding LLM Model Names appeared first on Analytics Vidhya.

At first, it looks like meaningless technical shorthand. Every part of that name tells you something about the model: how large it is, how it is built, how much of it is used at a time, how its weights are stored, and what format the file uses. Once you understand those pieces, choosing a local model becomes much easier. MoE: Does the Model Use Everything at Once? A3B: How Many Parameters Are Active? Base vs Instruct: How Was the Model Tuned? FP16, BF16: How Precisely Are Those Parameters Stored? Q4_K_M: What…

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