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Google's new open model DiffusionGemma generates text from noise instead of word by word

Summary

Google released DiffusionGemma, a 26-billion-parameter model that generates text via diffusion, achieving 1,000 tokens per second on an H100 GPU—four times faster than autoregressive models, but with lower quality. It's currently experimental.

SourceThe DecoderAuthor: Jonathan Kemper
Google's new open model DiffusionGemma generates text from noise instead of word by word
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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.

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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Key points

  • 26-billion-parameter diffusion model for text generation
  • Reaches 1,000 tokens/sec on a single H100 GPU
  • Lower output quality; positioned as experimental for developers

Highlights and analysis are generated automatically and may contain errors. Check the original source.