Google Releases Multi-Token Prediction Drafters for Gemma 4: Up to 3x Speedup
Original: Gemma 4 MTP released View original →
Gemma 4 Gets MTP Drafters
Google has released Multi-Token Prediction (MTP) drafters for the Gemma 4 model family. The announcement generated nearly 1,000 upvotes on r/LocalLLaMA, making it one of the week's most discussed local LLM releases.
Up to 3x Faster Without Quality Loss
MTP drafters use a specialized speculative decoding architecture: a smaller, faster draft model predicts several tokens ahead, which the target model verifies in parallel. Google reports up to a 3x speedup in tokens-per-second with no degradation in output quality or reasoning capability.
Technical Background
Standard LLM inference is memory-bandwidth bound — the processor spends most of its time moving billions of parameters from VRAM to compute units to generate each token. MTP relieves this bottleneck by reducing the number of full-model passes required, making better use of underutilized compute.
Available Models and Platforms
The released drafters support Gemma 4 31B-IT, 26B-A4B-IT (MoE), E4B, and E2B. All are available on Hugging Face. Speed improvements have been tested on LiteRT-LM, MLX, Hugging Face Transformers, and vLLM. Gemma 4, Google's most capable open model to date, reached 60 million downloads in just three weeks since launch.
Related Articles
A LocalLLaMA user has shared a detailed guide for running Qwen 3.6 27B with Multi-Token Prediction support in llama.cpp, achieving 2.5x inference speedup and 262k context on 48GB of memory.
Google DeepMind released DiffusionGemma, a 26B MoE open model that uses text diffusion instead of token-by-token decoding. The pitch is up to 4x faster generation on dedicated GPUs for local, interactive workflows.
Google has moved Gemini Enterprise Agent Platform evaluations to general availability, making agent quality measurable before and after launch on the same engine. The service combines more than 20 metrics, adaptive rubrics, simulators, telemetry scoring, and drift alerts.