The Serving Stack
Start hereCollectionsNewsletter
Sign in

awq

1 post

QUANTIZATION · 06INPUT SIZEWEIGHT COLUMNSSCALED UP BY s1% OF CHANNELS, CHOSEN BY THEIR INPUTS
quantizationawqllm-compressor

AWQ: Protecting the Weights That Matter

Which 1% of weights matter, why scaling them protects them, and the search behind AWQ, in a few lines of PyTorch on a real layer, then AWQ on Qwen3-8B with llm-compressor and vLLM against plain rounding, GPTQ and a published checkpoint.

by The Serving Stack

Inference Notes

Once a week: what changed in serving AI models, and what it means if you build, run or pay for them, in about five minutes.

The Serving Stack

Inference Engineering, In Depth

Navigate

HomeStart hereCollectionsNewsletterAll articles

Collections

Inference EnginesHow an LLM Request Becomes TokensLLMs, ExplainedThe GPU for Inference EngineersQuantization in PracticeServing in Production

Links

RSS Feed

Legal

Privacy PolicyTerms of ServiceCookie Policy
© 2026 The Serving Stack. All rights reserved.