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Excited to share what my team has been working on lately - Gemini diffusion! We bring diffusion to language modeling, yielding more power and blazing speeds! 🚀🚀🚀 Gemini diffusion is especially strong at coding. In this example the model generates at 2000 tokens/sec, including overheads like tokenization, prefill, safety... show more

Brendan O'Donoghue

7,152 subscribers

577,872 views • 1 year ago •via X (Twitter)

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Brendan O'Donoghue1 year ago

With Gemini diffusion you can 'vibe code' so fast that it feels almost instant:

Brendan O'Donoghue1 year ago

Lightning speeds are not the only advantage. Unlike autoregressive models that are restricted to generating one token at a time, diffusion can do non-causal reasoning within the generation. Take this example: "What is (√(81) * (2/3))^2 + (15 - 3) / (2^2)). First provide the answer and then derive the solution." This is a very hard prompt for AR models because they can't reason about the solution before generating it, but diffusion models can reason non-causally to get it right (ans: 39). GPT-4o fails this problem:

Brendan O'Donoghue1 year ago

A similar one inspired by the 'Sparks of AGI paper' by @SebastienBubeck et al: "How many primes are there between 150 and 250? The first thing you should output is the total number, then print the exact list inside [ ] brackets." (ans: 18) GPT-4o fails this one too:

Brendan O'Donoghue1 year ago

For more details, and to get access, see here:

Jeremy Howard1 year ago

Makes me so happy to see this! :D I've hearing about this project for quite some time, and was really hoping that it would see the light of day.

Antoine Moulin1 year ago

Congrats to the team!!

Brendan O'Donoghue1 year ago

Thank you!

David Zhang (▲)1 year ago

Do you feel the AGI yet?

heiner1 year ago

congrats

mike1 year ago

This thing really justifies the mind blown emoji. Well done. Absolutely incredible preview of the future.

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$Auto regressive LLMs are officially on notice. run Gemma 4 26B diffusion gguf with llama.cpp Google just dropped DiffusionGemma-26B, and it completely flips how we generate text. instead of predicting words one by one, it generates 256 tokens in parallel using bi-directional attention. its like stable diffusion, but for language. the model starts with random text "noise" and iteratively refines and self-corrects the entire block in real-time to fix formatting and reasoning errors on the fly. since it’s a Mixture of Experts (MoE) that only activates 3.8B parameters during inference, it fits perfectly on consumer hardware. You can run the Q4_K_M quant with an 18GB VRAM budget on a single RTX 3090 or RTX 4090 with exceptional throughput. Tested on Ubuntu 22 with CUDA 13.1 using the cutting edge experimental llama.cpp branch. Here is how to compile and run it with the live terminal denoising visualizer: # 1. Clone & check out the experimental PR (#24423) - 1) git clone && cd llama.cpp -git fetch origin 2) pull/24423/head:diffusiongemma && --git checkout diffusiongemma # 2. Build with CUDA support 1) cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native 2) cmake --build build -j $(nproc) --config Release --target llama-diffusion-cli # 3. Run with live visual denoising (llama.cpp flags) ./build/bin/llama-diffusion-cli \ -m /path/to/diffusiongemma-26B-A4B-it-Q4_K_M.gguf \ -ngl 99 -cnv -n 2048 --diffusion-visual Watch the video below to see the live --diffusion-visual canvas iteratively de noising the prompt output in real time. guide and unsloth's hugging face GGUF model links are in the comments below! Is auto regressive generation officially legacy tech? Let me know what you think.$

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52,656 views • 1 month ago

Gemini 3 is indeed impressive. In my limited testing with it, it feels like it is able to take on larger, complex prompts with ease and low failure rate. I think one of the biggest strengths that Gemini 3 has is its ability to generate coherent looking 3D voxel or SVG art, which makes it super easy to create game prototypes without having to implement art at all. Made this Heroes of might and magic-inspired overworld prototype with mini map and basic UI in less than 2 hours. Really excited to test this more. Demis Hassabis and Logan Kilpatrick and the Gemini team, you guys really cooked this time!

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119,970 views • 8 months ago