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Google DeepMind launches DiffusionGemma, boosting text generation speed by 4x

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On-chain news: Google DeepMind has launched DiffusionGemma, an open-source text generation model that increases speed by 4x. Using diffusion technology, it generates 256 tokens in parallel, achieving over 1,000 tokens/s on H100 and 700+ on RTX 5090. The 26B MoE model activates 3.8B parameters during inference and supports consumer GPUs after quantization. It features bidirectional attention and self-correction, making it ideal for code completion and inline editing. The model is open-sourced under Apache 2.0. New token listings may benefit from such advancements in generation efficiency.
ME AI News: Google DeepMind has released the open-source experimental model DiffusionGemma, which employs text diffusion technology to surpass autoregressive token-by-token generation by parallelly generating 256 tokens in a single forward pass. This 26B MoE model activates only 3.8B parameters during inference and, after quantization, fits on consumer-grade GPUs with 18GB VRAM. It achieves over 1000 tokens/s on H100 and over 700 tokens/s on RTX 5090, offering a 4x speed improvement. Featuring bidirectional attention and self-correction capabilities, it is designed for local interactive workflows such as inline editing and code completion, and is released under the Apache 2.0 license. (Source: AiHot)
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