Lesson 1354 of 2116
AI Model Quantization: 4-bit, 8-bit, FP16 Tradeoffs
How quantization affects quality, speed, and cost for self-hosted Llama, Mistral, and Qwen models.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2quantization
- 3FP16
- 4INT8
Concept cluster
Terms to connect while reading
Section 1
The premise
Quantization is the biggest lever in self-hosted inference economics — and the easiest to misconfigure.
What AI does well here
- Cut memory and cost dramatically with 4-bit weights.
- Maintain task quality on many use cases at INT8.
- Compare quants with task-specific evals.
What AI cannot do
- Promise zero quality loss across all tasks.
- Match FP16 quality on reasoning-heavy benchmarks.
End-of-lesson quiz
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