Decoding LLM Model Names: What Do GGUF, Q4_K_M, and A3B Actually Mean?
Murali Teja
Parameter Count: Parameter count indicates the number of learned values in a model, influencing its capabilities, memory requirements, and computational demands during inference.
Dense vs MoE: Dense models activate most parameters for each token, while Mixture of Experts models route tokens through selected expert networks.
Base vs Instruct: Base models predict text from training data, while instruct models undergo additional training to follow user instructions and respond helpfully.
Precision: Precision describes how numerical values are represented in model weights, affecting memory consumption, computational efficiency, and sometimes output quality during inference.
Quantization: Quantization reduces the numerical precision of model weights, lowering memory requirements and often improving inference speed, with potential accuracy trade-offs.
GGUF: GGUF is a model file format commonly used by llama.cpp, packaging model weights and metadata for efficient local inference across supported systems.
Q4_K_M: Q4_K_M is a four-bit quantization preset using mixed precision across selected tensors, balancing model size, inference efficiency, and quality.
A3B: A3B commonly indicates approximately three billion parameters activated per token in certain MoE models, though total parameters can be substantially higher.
Model Name Decoding: Reading model labels reveals architecture, parameter scale, tuning, and quantization, helping users select suitable models for hardware and intended workloads.