Lesson 2099 of 2116
AI Model Quantization: 8-bit, 4-bit, and Quality Cliffs
How quantization shrinks AI models for deployment — and where quality breaks.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2quantization
- 3int8
- 4int4
Concept cluster
Terms to connect while reading
Section 1
The premise
Quantization reduces AI model memory and improves throughput by storing weights in lower precision — int8 typically lossless, int4 hits noticeable quality cliffs on hard tasks.
What AI does well here
- int8: minimal quality loss across most workloads
- int4: usable for chat, classification, simple generation
- All: throughput gains on consumer GPUs
- Calibration-based methods preserve more quality
What AI cannot do
- Deliver flagship quality at int4 on hard reasoning tasks
- Recover lost capability without re-introducing precision
Key terms in this lesson
End-of-lesson quiz
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