NVIDIA Accelerates Google DeepMind's DiffusionGemma for Local AI

The new DiffusionGemma open model generates text in parallel - not one token at a time - and is optimized to run on the NVIDIA RTX PRO platform, NVIDIA DGX Spark systems and GeForce RTX GPUs. Today, Google DeepMind released DiffusionGemma - an experimental open model built for exceptionally fast text generation. NVIDIA has optimized DiffusionGemma to run even faster across NVIDIA GeForce RTX GPUs, the NVIDIA RTX PRO platform and NVIDIA DGX Spark systems, from local PCs to the cloud.

Rather than generating text one word at a time, DiffusionGemma generates multiple words in parallel to output whole blocks of text, opening a new, low-latency frontier for the kind of single-user workloads that developers, researchers and AI enthusiasts run every day. Features of the new model include: Parallel generation: DiffusionGemma denoises up to 256 tokens per step instead of predicting one at a time. Built on Gemma 4: DiffusionGemma is built on Gemma 4, a 26-billion-parameter mixture-of-experts model that activates just 3.8 billion parameters per step, pairing a diffusion head with Google's Gemma 4 architecture.

Up to 4x faster performance: The boost means fast text generation, where single-user generation usually stalls - on local hardware. Open and local: DiffusionGemma is open weights under a permissive Apache 2.0 license and runs entirely on RTX and DGX Spark - no cloud, no per-token cost - with day-zero support in Hugging Face Transformers , vLLM and Unsloth. A Different Way to Generate Text Almost every large language model (LLM) in wide use today is autoregressive - meaning it generates text one token at a time, with each new word depending on the one before it.

Each step denoises up to 256 tokens in parallel rather than emitting a single token and waiting to compute the next. The result is a model that thinks in blocks instead of sequentially. For latency-sensitive, single-user work - such as interactive chat, agentic loops or on-device assistants that plan and act - that parallelism translates into responses fast enough to keep pace with how developers think and iterate.

DiffusionGemma Flies on NVIDIA GPUs Generating one token at a time is fundamentally a memory-bound problem - a traditional LLM spends most of its time waiting on memory bandwidth, not doing math , which leaves a lot of compute on the table. Pulling a full 256-token block through the transformer in parallel is a compute-bound workload - exactly what NVIDIA GPUs are built for. NVIDIA Tensor Cores accelerate the dense parallel math, and the CUDA software stack lets the model run efficiently from day one without bespoke tuning.

In short, the model's design plays directly to the GPU ' ' s strengths. DiffusionGemma delivers 1,000 tokens/sec on a single NVIDIA H100 Tensor Core GPU, 150 tokens/sec on NVIDIA DGX Spark and up to 2,000 tokens/sec on NVIDIA DGX Station - roughly 4x faster than an equivalent autoregressive model running in the same single-user regime. That advantage holds across NVIDIA's full lineup, running : Locally on the NVIDIA DGX Spark deskside personal AI supercomputer - powered by the NVIDIA GB10 Grace Blackwell Superchip with 128GB of unified memory - with the preinstalled NVIDIA AI software stack ready for prototyping, fine-tuning and fully local agent workflows.