Lesson 957 of 1596
Speculative Decoding for Faster LLM Inference
How speculative decoding speeds up inference using a small draft model.
Creators · Model Families · ~7 min read
The premise
Speculative decoding can 2-3x inference throughput when configured well.
What AI does well here
- Use a small fast model to draft tokens for a large verifier.
- Maintain identical output distribution to the target model.
- Trade GPU memory for latency.
What AI cannot do
- Help when the draft model has poor agreement with the target.
- Improve quality — only speed.
Key terms in this lesson
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
- 1Ask AI to explain speculative decoding in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "Speculative Decoding for Faster LLM Inference" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check draft model against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
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