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Google DeepMind's DiffusionGemma achieves 1500 tokens/sec via discrete diffusion

By PulseAugur Editorial · [1 sources] ·

Google DeepMind has released DiffusionGemma, an open-weight language model that utilizes discrete diffusion for text generation, offering significantly faster output speeds compared to traditional autoregressive models. While DiffusionGemma achieves approximately 1,500 output tokens per second on a single NVIDIA H100, it scores lower on capability benchmarks than its autoregressive counterpart, Gemma 4. This innovation in serving speed is particularly beneficial for agent systems that require low latency and high throughput, suggesting a future where hybrid models or intelligent routing systems will select the appropriate generation method based on task requirements. AI

IMPACT Accelerates LLM serving speeds, particularly for agentic systems, by challenging traditional autoregressive generation.

RANK_REASON Frontier-lab model release with system card. [lever_c_demoted from frontier_release: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Google DeepMind's DiffusionGemma achieves 1500 tokens/sec via discrete diffusion

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Frontier-lab model release with system card. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Reid Marlow ·

    DiffusionGemma Is Fast Because It Stops Pretending Text Has to Be Written Left to Right

    <h1> DiffusionGemma Is Fast Because It Stops Pretending Text Has to Be Written Left to Right </h1> <p>Google DeepMind published DiffusionGemma this week, an open-weight language model that generates text with discrete diffusion instead of the usual token-by-token loop.</p> <p>Tha…

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