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Google’s Diffusion Gemma, its first open-weight diffusion-based language model released under Apache 2.0. I explain how diffusion decoding differs from autoregressive generation (parallel fixed-window generation that can revise earlier tokens), walk through the step mechanics (256-token patches, entropy/uncertainty locking with a budget, temperature cooling, early stopping), and why it becomes a hybrid: diffusion within blocks and autoregressive across blocks. I cover the MoE network details (26B total, ~4B active, 128 experts, sliding-window attention with periodic global layers, up to 256K context, small vision encoder), hardware/VRAM needs across BF16/FP8/NVFP4/GGUF, and day-one support in Transformers, vLLM, MLX, and llama.cpp. I also compare speed vs accuracy, show a local MLX demo UI, and generate a simple Pokémon website example.
https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/
https://huggingface.co/google/diffusiongemma-26B-A4B-it
https://ai.google.dev/gemma/docs/diffusiongemma
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Diffusion Gemma Explained: Google’s First Open-Weight Diffusion LLM (26B MoE) + Local Demo
00:00 Diffusion Gemma
01:02 Diffusion vs Autoregressive
02:03 How Diffusion Works
02:50 Inside a Denoising Step
04:08 Blocks and Hybrid Decoding
04:50 MoE Network Breakdown
05:37 Hardware and Quantization
07:06 Speed vs Accuracy Tradeoffs
08:14 Serving Options and Demo Setup
08:47 Parallel Generation Examples
09:53 Local UI and Coding Demo Read More Prompt Engineering
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