Google DeepMind's DiffusionGemma Hits 1,500 Tokens/sec Using Parallel Diffusion Decoding

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Google DeepMind released DiffusionGemma this week, an open-weight language model built on Gemma 4 26B that generates text using discrete diffusion rather than traditional left-to-right autoregressive decoding. Instead of producing one token at a time, the model works on a 256-token block and denoises it in roughly 12 parallel steps, yielding around 20 tokens per forward pass. On a single H100 GPU, the model achieves approximately 1,500 output tokens per second, compared to about 303 tokens per second for a comparable Gemma 4 autoregressive setup. However, the speed gains come with capability trade-offs — DiffusionGemma scores lower than the Gemma 4 baseline on benchmarks like AIME 2026 and GPQA Diamond. The throughput advantage also diminishes at batch sizes above roughly 32 concurrent users, suggesting the model is best suited for low-concurrency, latency-sensitive applications such as multi-step AI agent workflows.

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DeepMind Splits Robot Brain and Body With Three-Model Gemini Robotics 2 Suite

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Your Phone Probably Isn't Listening — But It Doesn't Need To

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