GGUF vs GPTQ vs AWQ vs EXL2: LLM Model Formats Explained

Understanding quantization format tradeoffs is critical for anyone deploying LLMs locally. This guide provides a detailed breakdown of the four dominant formats: GGUF (widely supported and optimized for CPU inference), GPTQ (GPU-focused with excellent compression), AWQ (newer format with strong accuracy preservation), and EXL2 (specialized for extreme compression). Each format offers different speed, memory, and accuracy characteristics depending on your hardware and latency requirements.

For local deployment practitioners, choosing the right format directly impacts whether a model will run on your hardware at all. GGUF remains the gold standard for cross-platform compatibility and CPU inference, while GPTQ dominates in GPU environments. Understanding these distinctions helps teams avoid dead ends in their inference pipeline and select formats with good community support and tooling around llama.cpp, vLLM, and other inference engines.

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Source: Google News · Relevance: 10/10