Diffusion Gemma: Google's First Open Diffusion Model
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 My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: engineerprompt@gmail.com Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 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
