Gemma 4 MTP Local Test | Multi-Token Prediction of E2B using HuggingFace Transfomers | 🔴 Live

2026-05-06 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

Google has released drafter models for multi-token prediction (MTP) for its Gemma 4 models, significantly improving output token speed. These drafter models enable a roughly 2x speed improvement for local or self-hosted Gemma 4 models, requiring only a small amount of additional memory (e.g., a 78 million parameter drafter for a 2 billion parameter model). The MTP functionality, based on speculative decoding, allows a smaller model to predict tokens ahead of time, which are then verified by the larger model, accelerating overall inference. Benchmarking on a 2 billion parameter Gemma 4 model demonstrated speedups ranging from 2x for general text and PyTorch code generation to nearly 3x for JSON extraction, with a negligible increase in VRAM usage (approximately 0.2 GB). The implementation is currently available via the Hugging Face Transformers library, with ongoing efforts for GGUF (llama.cpp) and MLX support.

Key takeaway

For ML Engineers deploying Gemma 4 models locally or self-hosting, integrating the new multi-token prediction drafter models is crucial. This upgrade delivers a substantial 2-3x increase in output token speed for a minimal VRAM cost (around 0.2 GB), making Gemma 4 significantly more efficient for various tasks, including code generation and JSON extraction. You should explore the Hugging Face Transformers implementation to immediately benefit from these performance gains.

Key insights

Gemma 4 models achieve 2-3x inference speedup via multi-token prediction using small drafter models and speculative decoding.

Principles

• Speculative decoding accelerates LLM inference.
• Small drafter models enable significant speed gains.

Method

A smaller drafter model predicts tokens ahead of the main model. If verified, these predictions are accepted, effectively speeding up the overall inference process without substantial memory overhead.

In practice

• Utilize Hugging Face Transformers for Gemma 4 MTP.
• Expect 2-3x speedup with minimal VRAM increase.

Topics

• Gemma 4 Models
• Multi-Token Prediction
• Speculative Decoding
• Hugging Face Transformers
• LLM Inference Speedup

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.