AI Speculative Decoding Tutorial: Faster Inference
TL;DRSpeculative decoding speeds inference by drafting with a small model and verifying with the big one.
Generating tokens one-by-one is slow. Speculative decoding uses a small, fast "draft" model to propose several tokens ahead, then the large model verifies them in one pass — accepting the correct prefix and only recomputing where they differ. The output is identical to the big model's, but often 2-3× faster. It's a lossless latency optimization widely used in production serving.
Key points
Small model drafts several tokens ahead
Big model verifies them in one pass
Identical output, often 2-3× faster
Lossless production latency win
Common mistakes
Assuming it changes the output (it doesn’t)
Using a draft model too divergent from the target
Ignoring it as a free speedup
Try it: Explain why speculative decoding is lossless yet faster.
Example code
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<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>small model drafts: "the cat sat on"
big model verifies all at once → accept correct prefix
same output, fewer big-model steps</pre></body></html>