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DiffusionGemma can now run at 2000+ tokens/sec! ⚡ We made local DiffusionGemma inference 1.8× faster. Run it on 18GB RAM via Unsloth Studio. GitHub: Guide:

Unsloth AI

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180,842 görüntüleme • 3 ay önce •via X (Twitter)

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Philipp Schmid profil fotoğrafı

Philipp Schmid3 ay önce

Wow!

Tery Emilson profil fotoğrafı

Tery Emilson3 ay önce

I think batch speed is the boring win compared with always-on local AI. A model you poke once can afford to be slow. A model running continuously in the background ... watching context, briefing you before things happen ... that's where 2000 tok/s is the line between a chatbot and an ambient assistant. Well done! (again!)

Maziyar PANAHI profil fotoğrafı

Maziyar PANAHI3 ay önce

2000+ tokens/sec! wow! you know, i would die for a diffusion model like this on my iOS! ps: of course with a unsloth fine-tuning recipe! 🤩

Apollo profil fotoğrafı

Apollo3 ay önce

isnt DiffusionGemma more prone to lower quality output though? Google’s own launch post says DiffusionGemma is optimized for speed and that its overall output quality is lower than standard Gemma 4. Google also says standard Gemma 4 remains the better choice for applications that need maximum quality.

Terp profil fotoğrafı

Terp3 ay önce

??? im getting 170 usable tps on my 5090 compared to 500+ through vllm what's the issue ?

Le TechLead🔰 profil fotoğrafı

Le TechLead🔰3 ay önce

@danielhanchen we need to be able to serve it though, cli and chat doesn’t cut it.

Xaden Ryan profil fotoğrafı

Xaden Ryan3 ay önce

@danielhanchen Does it do tool calling?

Ankit Prateek profil fotoğrafı

Ankit Prateek3 ay önce

llama-server still doesn't support diffusion model. mlx does but token gen speed is horrible.

Tarrito.rocks profil fotoğrafı

Tarrito.rocks3 ay önce

Nice but I didn't find UD-Q4 model version in your repo

Dariton profil fotoğrafı

Dariton3 ay önce

Does this work with CPU offloading though?

Ankit Prateek profil fotoğrafı

Ankit Prateek3 ay önce

This is wild

Ankit Prateek profil fotoğrafı

Ankit Prateek3 ay önce

I spent ~6 hours making this diffusion model work on my mac, and that gave me 10 tokens/s because there was no llama.cpp support lol

ibrand profil fotoğrafı

ibrand3 ay önce

It needs to run comfortably on 16gb. Hardly anyone has 18

Piyush profil fotoğrafı

Piyush3 ay önce

any quantized version available that will enable it run on T4?

AACeeert profil fotoğrafı

AACeeert3 ay önce

An abliterated version of this will have malware scripts flying around the internet in milliseconds

Eric ⚡️ Building... profil fotoğrafı

Eric ⚡️ Building...3 ay önce

WOW

Vabbyshabby profil fotoğrafı

Vabbyshabby3 ay önce

2000 tok/s local is the actual answer to this morning's news. nobody export-controls a gguf on your own box. this is the lane.

Emircan ERKUL profil fotoğrafı

Emircan ERKUL3 ay önce

cant you fit that into 14gb so i could use with 16vram gpu

mr_r0b0t profil fotoğrafı

mr_r0b0t3 ay önce

Cooking with white hot 🔥🔥🔥🔥

Anis🐬Al profil fotoğrafı

Anis🐬Al3 ay önce

My sister, this is truly exhilarating news! 🌟 Seeing DiffusionGemma achieve such breathtaking speeds—surpassing 2000 tokens per second—while remaining accessible on local hardware like 18GB RAM is a masterpiece of efficiency over sheer bulk. It’s not just about the technical milestones; it's about the democratization of intelligence. By bridging the gap between high-performance research and local accessibility, you are helping to put the pulse of innovation directly into our hands. This transition from massive cloud dependency to agile, local execution is where technology truly begins to serve humanity with grace and speed. Keep pushing these boundaries! ✨

Secta profil fotoğrafı

Secta3 ay önce

local diffusiongemma inference at 2000+ tokens/sec is a clear win low ram threshold shifts deployment from cloud to edge

Pranav profil fotoğrafı

Pranav3 ay önce

Is Gemma4 12B coming, based on this diffusion tech? 🤔

netrunner profil fotoğrafı

netrunner3 ay önce

wait this runs on 18gb?

ArdanZ profil fotoğrafı

ArdanZ3 ay önce

My GPU only 12GB Vram 😭

Robert Keyes profil fotoğrafı

Robert Keyes3 ay önce

Have you been able to fix the slop it slings? Last I saw was terrible decode.

Kaustubh Joshi profil fotoğrafı

Kaustubh Joshi3 ay önce

Fast inference is exciting — but what you prompt it with still determines the output quality. ⚡ Save your best DiffusionGemma prompts and never lose them at — free prompt management for AI power users. 🚀 #DiffusionGemma #UnslothAI #PromptEngineering

Sanjay profil fotoğrafı

Sanjay3 ay önce

2000 tokens/sec on 18GB RAM is actually insane. local AI just quietly won

oriel haim profil fotoğrafı

oriel haim3 ay önce

Details!!!

AI Mastery Guide profil fotoğrafı

AI Mastery Guide3 ay önce

2000+ tokens per second locally on 18GB RAM is not a small deal. The gap between local and cloud is closing faster than most people expected.

Gerladina profil fotoğrafı

Gerladina3 ay önce

local inference keeps getting more realistic 18gb ram opens this up to way more people now

Twon. profil fotoğrafı

Twon.3 ay önce

How fast on a 3090?!

Thor 雷神 ⚡️ profil fotoğrafı

Thor 雷神 ⚡️3 ay önce

Yooo, that's very unsloth 🚀

Thomas Linden profil fotoğrafı

Thomas Linden3 ay önce

Google’s tournament style idea generation would go crazy with diffusion models

Verma profil fotoğrafı

Verma3 ay önce

Wow 🔥

Adel Bucetta profil fotoğrafı

Adel Bucetta3 ay önce

because the hard part was always scaling diffusers, 2000 tokens/sec changes everything

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GOOGLE JUST MADE EVERY CHATBOT FEEL SLOW Diffusion Gemma 26b doesn’t predict word by word, it generates 256 tokens in parallel using bi-directional attention, like stable diffusion but for language it’s MoE so only 3.8B params activate during inference, fits on a single RTX 4090 with 18GB VRAM and you can run it right now via llama.cpp, the same engine powering every tool in my local llm playbook the whole ecosystem exists for moments like this ↓

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