93GPTQ vs AWQ: how do these post-training quantization methods differ, and when do you pick each?▼hardNVIDIAHugging FaceAWS1 replies◆ premiumBoth squeeze an LLM to 4-bit weights, but they decide what to protect very differently. The signal is GPTQ's error-correcting solve versus AWQ's activation-aware scaling, and the calibration each needs.Open full answer →
03Explain quantization for inference: INT8/INT4, GPTQ/AWQ, what breaks, and how you validate it.▼hard★ EssentialNVIDIAOpenAIxAI2 repliesunlockedQuantization is the first lever for fitting and speeding up models, and the interviewer wants more than 'use fewer bits.' The signal is knowing what precision buys you, why outliers break naive quantization, and how you prove quality held. Here is that answer.Open full answer →
