Google released DiffusionGemma, a 26-billion-parameter model that generates text not token by token but through diffusion, similar to how image AI turns noise into a picture. According to Nvidia, it hits about 1,000 tokens per second on a single H100 GPU, roughly four times faster than comparable autoregressive models. The speed comes at a cost, though. Output quality is lower, so Google is positioning it as an experimental tool for developers for now.<br /> The article Google's new open model DiffusionGemma generates text from noise instead of word by word appeared first on The Decoder. [...]
GenAI image generators like Stable Diffusion do not draw a picture pixel by pixel from left to right. They start with noise and iteratively refine the entire image in parallel until it converges, in a [...]
Rarely does a set of open-fit earbuds actually impress me. I tend to find them underwhelming because overall sound quality is subpar compared to the more “traditional” in-ear models. Any promise o [...]
Instead of training a new model from scratch, Google DeepMind retrofitted Gemma 4 into a diffusion model using less than 10 percent of the original training budget. DiffusionGemma generates 256 tokens [...]