Accelerating Gemma 4: faster inference with multi-token prediction drafters

2026-05-05 · Source: The Keyword · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Google has released Multi-Token Prediction (MTP) drafters for its Gemma 4 family of open models, significantly accelerating inference speed by up to 3x without compromising output quality or reasoning. Gemma 4, introduced weeks prior, has already seen over 60 million downloads. The MTP drafters employ a specialized speculative decoding architecture, addressing the memory-bandwidth bound nature of standard LLM inference. This technique pairs a heavy target model, such as Gemma 4 31B, with a lightweight MTP drafter to predict multiple future tokens simultaneously. The target model then verifies these suggested tokens in parallel, allowing applications to output a full drafted sequence plus one additional token in the time typically required for a single token. This enhancement improves responsiveness for real-time chat, supercharges local development on consumer GPUs, and boosts on-device performance for edge models like E2B and E4B.

Key takeaway

For AI Architects and NLP Engineers deploying Gemma 4 models, integrating MTP drafters is crucial for optimizing inference performance. This allows for up to a 3x speedup, enabling more responsive applications and efficient local or edge deployments without sacrificing model accuracy. You should explore the provided documentation and download MTP drafters from Hugging Face or Kaggle to implement faster inference across various platforms like MLX, VLLM, and Ollama.

Key insights

Multi-Token Prediction drafters accelerate Gemma 4 inference up to 3x using speculative decoding without quality loss.

Principles

• Decouple token generation from verification.
• Utilize idle compute for speculative prediction.
• Maintain quality through primary model verification.

Method

Speculative decoding pairs a lightweight drafter model to predict multiple tokens, which a heavier target model then verifies in parallel, accepting correct sequences in a single pass.

In practice

• Run Gemma 4 26B/31B models faster on consumer GPUs.
• Enhance responsiveness for real-time AI applications.
• Improve battery life for on-device E2B/E4B models.

Topics

• Gemma 4
• Multi-Token Prediction
• Speculative Decoding
• LLM Inference Speed
• Edge AI Performance

Best for: AI Architect, NLP Engineer, AI Product Manager, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Keyword.