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How Multi-Token Prediction drafters accelerate Gemma 4 inference by up to 3x

By

Olivier Lacombe

26d ago· 4 min readenInsight

Summary

This article explains how Google's Gemma 4 models achieve up to 3x faster inference through Multi-Token Prediction (MTP) drafters and speculative decoding. It describes the technical approach where smaller draft models predict multiple tokens simultaneously, which are then verified by the main model, overcoming the memory-bandwidth bottleneck that typically limits LLM inference speed. The article covers the architecture, training methodology, and performance benefits of this approach.

Key quotes

· 3 pulled
The processor spends the majority of its time moving billions of parameters from VRAM to the compute units just to generate a single token.
Standard LLM inference is memory-bandwidth bound, creating a significant latency bottleneck.
Multi-Token Prediction (MTP) drafters are making Gemma 4 models up to 3x faster at inference.
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An overview of how Multi-Token Prediction (MTP) drafters are making Gemma 4 models up to 3x faster at inference.

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