Q4 vs Q6 vs Q8: The Quantization Decision Framework for Local LLMs

Quantisation remains the most practical way to run large models on consumer hardware, but choosing between Q4, Q6, and Q8 formats requires understanding the quality-speed-memory tradeoffs. This framework breaks down the technical differences, empirical performance characteristics, and real-world deployment scenarios where each quantisation level excels.

The guide examines how quantisation levels affect different model architectures and task types, from general chat and coding to instruction-following and reasoning. Q4 quantisation can reduce model size by 75% with acceptable quality loss for many applications, Q6 offers a middle ground, while Q8 provides near-original precision at minimal compression. The analysis includes benchmarks across popular local inference engines like llama.cpp and Ollama.

For local LLM practitioners, this decision framework helps eliminate guesswork when optimising models for specific hardware. Understanding these tradeoffs is fundamental to deploying production-ready systems on-device, enabling informed choices about model selection, quantisation, and hardware requirements.

Source: SitePoint · Relevance: 10/10