GPTQ vs AWQ: how do these post-training quantization methods differ, and when do you pick each?
Both compress an LLM to 4-bit weights, yet they choose what to protect in very different ways. What matters is GPTQ's error-correcting solve versus AWQ's activation-aware scaling, and the calibration each demands.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Both compress an LLM to 4-bit weights, yet they choose what to protect in very different ways. What matters is GPTQ's error-correcting solve versus AWQ's activation-aware scaling, and the calibration each demands.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
