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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

98,289 subscribers

180,842 Aufrufe • vor 3 Monaten •via X (Twitter)

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35 Kommentare

Profilbild von Philipp Schmid

Philipp Schmidvor 3 Monaten

Wow!

Profilbild von Tery Emilson

Tery Emilsonvor 3 Monaten

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!)

Profilbild von Maziyar PANAHI

Maziyar PANAHIvor 3 Monaten

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! 🤩

Profilbild von Apollo

Apollovor 3 Monaten

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.

Profilbild von Terp

Terpvor 3 Monaten

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

Profilbild von Le TechLead🔰

Le TechLead🔰vor 3 Monaten

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

Profilbild von Xaden Ryan

Xaden Ryanvor 3 Monaten

@danielhanchen Does it do tool calling?

Profilbild von Ankit Prateek

Ankit Prateekvor 3 Monaten

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

Profilbild von Tarrito.rocks

Tarrito.rocksvor 3 Monaten

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

Profilbild von Dariton

Daritonvor 3 Monaten

Does this work with CPU offloading though?

Profilbild von Ankit Prateek

Ankit Prateekvor 3 Monaten

This is wild

Profilbild von Ankit Prateek

Ankit Prateekvor 3 Monaten

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

Profilbild von ibrand

ibrandvor 3 Monaten

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

Profilbild von Piyush

Piyushvor 3 Monaten

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

Profilbild von AACeeert

AACeeertvor 3 Monaten

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

Profilbild von Eric ⚡️ Building...

Eric ⚡️ Building...vor 3 Monaten

WOW

Profilbild von Vabbyshabby

Vabbyshabbyvor 3 Monaten

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.

Profilbild von Emircan ERKUL

Emircan ERKULvor 3 Monaten

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

Profilbild von mr_r0b0t

mr_r0b0tvor 3 Monaten

Cooking with white hot 🔥🔥🔥🔥

Profilbild von Anis🐬Al

Anis🐬Alvor 3 Monaten

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! ✨

Profilbild von Secta

Sectavor 3 Monaten

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

Profilbild von Pranav

Pranavvor 3 Monaten

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

Profilbild von netrunner

netrunnervor 3 Monaten

wait this runs on 18gb?

Profilbild von ArdanZ

ArdanZvor 3 Monaten

My GPU only 12GB Vram 😭

Profilbild von Robert Keyes

Robert Keyesvor 3 Monaten

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

Profilbild von Kaustubh Joshi

Kaustubh Joshivor 3 Monaten

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

Profilbild von Sanjay

Sanjayvor 3 Monaten

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

Profilbild von oriel haim

oriel haimvor 3 Monaten

Details!!!

Profilbild von AI Mastery Guide

AI Mastery Guidevor 3 Monaten

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.

Profilbild von Gerladina

Gerladinavor 3 Monaten

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

Profilbild von Twon.

Twon.vor 3 Monaten

How fast on a 3090?!

Profilbild von Thor 雷神 ⚡️

Thor 雷神 ⚡️vor 3 Monaten

Yooo, that's very unsloth 🚀

Profilbild von Thomas Linden

Thomas Lindenvor 3 Monaten

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

Profilbild von Verma

Vermavor 3 Monaten

Wow 🔥

Profilbild von Adel Bucetta

Adel Bucettavor 3 Monaten

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

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