DeepMind releases DiffusionGemma, a text model up to 4x faster

DeepMind has introduced DiffusionGemma, an experimental open model that applies diffusion to text generation. Rather than writing word by word, as conventional language models do, it works on entire blocks of up to 256 tokens—text fragments—and refines them over several passes.

The company says this approach delivers generation speeds of up to four times faster on a dedicated GPU. The announcement matters less as a replacement for today’s large language models than as an exploration of an alternative for tasks where immediate responses, editing and local execution matter more than achieving the highest possible quality.

A 26-billion-parameter model that activates 3.8 billion

DiffusionGemma is a mixture-of-experts model, an architecture that brings together several specialized submodels but activates only some of them for each operation. It has 26 billion parameters in total, although it activates 3.8 billion during inference.

That difference reduces the cost of running the model. DeepMind says that, when quantized—meaning it uses lower-precision numbers to save memory—it can run within 18 GB of video memory. That puts it within reach of computers with high-end graphics cards, although it does not make local deployment trivial: it still requires specialized hardware and the right setup.

According to figures published by DeepMind, the model exceeds 1,000 tokens per second on an NVIDIA H100 and 700 tokens per second on a GeForce RTX 5090. These measurements were taken on specific accelerators and do not necessarily correspond to the speed a user will see in every application: final performance depends on text length, quantization, inference software and workload.

From sequential text to parallel refinement

Conventional assistants generate text autoregressively: they predict one token, add it to the context and repeat the process. It is an effective method and the standard for producing high-quality responses, but it requires waiting for each step before generating the next.

Diffusion changes that mechanism. The model starts with unresolved text positions and iteratively corrects them until it produces a coherent output. By processing an entire block, each position can take the others into account. This bidirectional attention can be useful in tasks where linear order is a limitation: completing code in the middle of a file, editing a sentence without rewriting the rest, generating Markdown structures or solving sequences with global constraints.

DeepMind also cites code infilling, amino acid sequences and mathematical graphs. As a fine-tuning demonstration, Unsloth has adapted the model to solve Sudoku puzzles, a problem in which an initial decision depends on cells that have not yet been filled in.

The speed advantage does not extend equally to every environment

The key caveat of the release is where that advantage applies. Parallel generation makes better use of a GPU when it is handling few requests, as on a personal computer, workstation or interactive application for a single user.

In cloud services handling thousands of requests, autoregressive models can batch many conversations together and use hardware efficiently. In that scenario, DiffusionGemma’s advantage diminishes and the cost of serving each response may even increase. It is therefore not a universal solution for making large-scale chatbots cheaper.

Nor is it intended to be DeepMind’s highest-quality model. The company itself acknowledges that its overall output falls below that of autoregressive Gemma 4 models and recommends the latter for production uses that require the best possible response. DiffusionGemma prioritizes speed and parallel generation, two goals that still involve trade-offs in open-ended, complex text.

Open weights and room to experiment

The weights are being released under the Apache 2.0 license, a permissive license that allows users to use, modify and distribute the software with few restrictions. The model will be available on Hugging Face and will include runtime tools through MLX, vLLM and Hugging Face Transformers; DeepMind also says support for llama.cpp is coming.

DiffusionGemma’s significance lies in bringing to text models an idea popularized by image generators: producing an answer through refinement rather than through a single chain of predictions. If the approach demonstrates sufficient quality in editing, programming or local interfaces, it could open up a category of assistants that are less like a typewriter and more capable of reviewing an entire page before delivering it.