Diffusion Gemma: Google's First Open Diffusion Model

Prompt Engineering · Beginner ·🧠 Large Language Models ·1mo ago

About this lesson

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 04:5

Original Description

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 04:5
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
No AI Claim Without a Kill Condition: Falsifier-Driven AI Decisions
Learn to implement falsifier-driven AI decisions to ensure reliable outcomes by adding a structural gate with an explicit kill condition
Dev.to · John
 📰
I Got Tired of Comparing AI Models Across Multiple Browser Tabs, So I Built AutarkChat
Learn how AutarkChat simplifies AI model comparison and experimentation, and how you can apply similar principles to your own projects
Dev.to AI
 📰
Cross-Modal Knowledge Distillation for heritage language revitalization programs with inverse simulation verification
Learn how to apply Cross-Modal Knowledge Distillation with inverse simulation verification for heritage language revitalization programs and understand its significance in AI and language preservation
Dev.to AI
 📰
Transfer Learning in LLM: Concepts and Applications
Apply transfer learning in LLMs using retrieval-augmented generation to build domain-specific support agents without fine-tuning foundation models
Dev.to AI

Chapters (5)

Diffusion Gemma
1:02 Diffusion vs Autoregressive
2:03 How Diffusion Works
2:50 Inside a Denoising Step
4:08 Blocks and Hybrid Decoding
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →