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Speculative Decoding Boosts Gemma 4 31B Speed by 29% Average, 50% on Code Tasks
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Speculative Decoding Boosts Gemma 4 31B Speed by 29% Average, 50% on Code Tasks

推測解碼讓 Gemma 4 31B 快了 29%，寫程式碼快 50%

I tested speculative decoding—a technique where a smaller AI model predicts what a larger one will say next—using Gemma 4's tiny 4.65B draft model to speed up the full 31B version. The results blew my expectations out of the water. On an RTX 5090, the setup achieved a 29% average speed boost across general tasks, and a massive 50% improvement specifically on code generation. The smaller draft model essentially acts like a smart shortcut, letting the main model skip ahead when it's confident about the prediction. If you're running large language models locally and care about speed, this is a game-changer worth testing.

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Keywords

speculative decodingGemma 4 31Bdraft modelinference optimizationthroughput improvementcode generationRTX 5090TurboQuant

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