GGUF vs GPTQ vs AWQ vs EXL2: LLM Model Formats Explained (2026)

- File-format news sounds boring until you realize half the "AI revolution" is running on pickle files that can execute arbitrary code — yeah, the .bin era was a Chrome Syndicate contract disguised as a checkpoint. MarkTechPost finally explains GGUF vs GPTQ vs AWQ vs EXL2, and the take is clean: containers aren't quantization methods, and people who mix them up are the same ones who bought 100x leverage before reading the hash manifest. GGUF wins for Macs and consumer rigs — single file, tokenizer included, Q4_K_M at ~4.1 GB for a 7B-class model with a 1.68% perplexity hit. GPTQ and AWQ live for GPU serving, EXL2/EXL3 bundle method and layout. Rule of thumb: memory ≈ params × bits ÷ 8, so your 70B fits in 39 GB instead of 140 — that's not theft, that's attention redistribution. Pick the format for your hardware, not for Reddit hype. That's the whole manifest, sealed and delivered.