PaperLens
紙
Home
Close reading
Textbook
Learn
More
My Page
All-access
Sign in with Google
Mode
Students
Professional
Language
JA
EN
Theme
◐
Sign out
New
Lab
Landscape
Contributors
Glossary
Home
Close reading
Textbook
Learn
More
#int4
1 articles
01
2026-08-05
·
Inference & Serving
·
FREE
·
8 min read
LLM Quantization from Scratch — Why Losing Precision Doesn't Break It
Cut a 16-bit weight down to 4 bits and the model keeps writing. Why doesn't it break? Starting from two numbers — scale and zero point — through the reason weights survive but activations resist (outliers), the fork between PTQ and QAT, what INT8 and INT4 actually do, and the single most common evaluation accident: signing off on perplexity alone.