Google's new open model DiffusionGemma generates text from noise instead of word by word
Quick Answer
Google has launched DiffusionGemma, a 26-billion-parameter model that generates text through diffusion rather than token-by-token, achieving around 1,000 tokens per second on a single H100 GPU.
Quick Take
Google has launched DiffusionGemma, a 26-billion-parameter model that generates text through diffusion rather than token-by-token, achieving around 1,000 tokens per second on a single H100 GPU. While it operates four times faster than traditional autoregressive models, the output quality is lower, making it an experimental tool for developers.
Key Points
- DiffusionGemma uses a novel diffusion approach for text generation.
- The model has 26 billion parameters and generates text at 1,000 tokens per second.
- It is four times faster than comparable autoregressive models.
- Output quality is lower, limiting its current use to experimental purposes.
- Google targets developers with this new tool for further exploration.
Article Excerpt
From source RSS / original summaryGoogle 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.
The article Google's new open model DiffusionGemma generates text from noise instead of word by word appeared first on The Decoder.
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