How Multi-Token Prediction drafters accelerate Gemma 4 inference by up to 3x
By
Olivier Lacombe
Right out the toaster. Reliable, with some real depth.
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 pulledThe 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.
You might also wanna read
PostHog plans to train its own AI models for proactive, self-driving product features
PostHog is entering a new phase focused on building proactive, self-driving AI-powered products. After launching popular AI features like th
PostHog plans to train its own AI models for proactive, self-driving product features
PostHog is entering a new phase focused on building proactive, self-driving AI-powered products. After launching popular AI features like th
Models.dev: An open-source community database for AI model specifications and pricing
Models.dev is an open-source, community-contributed database that aggregates AI model specifications, pricing, and capabilities from various
ModelHub: A macOS menu bar app for managing local LLMs across Ollama, MLX, and LM Studio
ModelHub is a native macOS menu bar app designed to streamline the workflow for developers working with local LLMs. It addresses the fragmen
LLMTest: Automated LLM Model Selection and Fallback Tool for Developers
LLMTest is a tool created by maker Tom to help developers and "vibe coders" automatically select the best LLM models for AI-powered features
Building a local video archive with Gemma 4 31B on a 2021 M1 Max MacBook Pro
A detailed technical account of building a local, queryable video archive on a 2021 MacBook Pro M1 Max using Google's Gemma 4 31B model in L
