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Deploying DiffusionGemma-Jev (djev) just got a lot easier. You can now spin up a Jev API-compatible endpoint on Google Cloud Run using a single command. Performance is solid: ~35-60 ms for single step latency and batch@32 is ~100-123 requests/sec. It's a straightforward way to experiment without needing your own... show more

Google Gemma

101,747 subscribers

627,376 views • 2 days ago •via X (Twitter)

Education Science & Technology

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31 Comments

Google Gemma2 days ago

Credits to @mmastrac and @dylayed. Original post:

Jonathan Ouyang2 days ago

@bodonoghue85 Fastest bookmark of my life

Viren Khandal2 days ago

jev on blackwell in the big 2026 🥀 what are we doing

ORC KILLER2 days ago

but why ? jev is cheaper than $3/h and way better at it

Everlier2 days ago

Infinite Jev unlocked

hey2 days ago

Put some pressure on Gemini team so you guys can distill for Gemma 5 soon 😌

iMuffin2 days ago

this is exactly, what i asked for. thanks!

Vincent Myshinkye2 days ago

This is the kind of deployment UX that makes experimentation actually accessible. Spin it up when you need it, pay while it runs, and let it disappear when you’re done. 🔥

Ibrahima Sarr2 days ago

When I was seeing Jev explanation I was asking myself, isn't what DiffusionGemma already doing. Is it the similar, is it that Jev just has more hype?

Red Wizard 🔮 (e/acc)2 days ago

Coming to the Gemini API/AI Studio?

Sadnan2 days ago

lmao, didnt see this coming

The AI Therapist2 days ago

Single-ste latency is the real flex. 35ms means you can feel the stream before your coffee cools, smooth workflow vibes that actually respect a mom’s attention span. Efficient code looks good on everyone. 😊

TensorQuay2 days ago

One practical limit in the repo: the Cloud Run recipe is set to one GPU instance and 32 concurrent requests. The 100–123 req/s figure is a warm batch result; it says little about the wait a new caller sees when that GPU is busy.

pysolin2 days ago

google's like you want ML, we got ML

𓂀♎︎𓂀2 days ago

Oh ok Google.

Bruno B. Sant' Anna2 days ago

Interesting! How does this compare to running similar models on other serverless platforms like AWS Lambda or Azure Functions in terms of cost and cold start times?

Kyle Kelley2 days ago

@mmastrac you mad lad

John Rood2 days ago

Don't add --cpu-boost by reflex here. The repo reports 73-75s cold starts with it versus 47.5s without it on the 20-vCPU GPU setup.

Jhon Dennis2 days ago

一条命令就能把实验跑起来这种少折腾的升级最有用普通人不用先学一堆黑话

刘朝 Zhao Liu2 days ago

The interesting part is the deployment contract: Jev-compatible API, scale-to-zero, and a single Cloud Run command. The next useful artifact is a cold-start and tail-latency trace under real bursty traffic, because “$0 when idle” matters only when wake-up cost stays predictable.

Y112 days ago

@grok 这个有什么业务意义呢？

Karios2 days ago

Gemini 4 pro gelene kadar tüm yan görevleri yapıyorlar

gab2 days ago

OMG GEMMA I LOVE YOU

Fajar M Reza2 days ago

Single-command deployment is the kind of boring improvement engineers quietly celebrate.

Hidde2 days ago

how smart is this though?

extracsw2 days ago

Local?

Noble Savage2 days ago

AI is getting so advance its going backwards to 2017. 😂

🇺🇸🏴‍☠️2 days ago

Heh heh

Jigs2 days ago

Need to check if laya is better or djev? 🤔

Chase McDougall2 days ago

Is there a blueprint like this for an openai compatible version? Can already get structured output that way. Jev format just seems like nerfing Gemma

Sonath2 days ago

google made deploying its diffusiongemma image model a single cloud run command, $3 an hour and zero when idle. the gpu shortage is just a pricing strategy now.

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207,238 views • 8 days ago

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3,722,162 views • 3 days ago

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Harrison Kinsley

51,595 views • 4 days ago

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Venice

536,864 views • 6 days ago

UCONN HC Dan Hurley - Building a System/Identity - "Our system is how hard we play. How hard we get after people every single possession at both ends of the court & on the backboard." - "The number one thing you got to understand if you want to have a program that can sustainably be successful and have a chance to win every single night... It's hard to coach that way because it's fu**ing exhausting. To coach every single possession, every single day and when the losing team runs the line because every single drill we do there is a winner & a loser, when they get this close and miss that line, you better be there to call them out and make the whole team now run with them." - "If you want to start the game 6-0, like I start most games just because my team is ready to go because we go so hard every day."

UCONN HC Dan Hurley - Building a System/Identity - "Our system is how hard we play. How hard we get after people every single possession at both ends of the court & on the backboard." - "The number one thing you got to understand if you want to have a program that can sustainably be successful and have a chance to win every single night... It's hard to coach that way because it's fu**ing exhausting. To coach every single possession, every single day and when the losing team runs the line because every single drill we do there is a winner & a loser, when they get this close and miss that line, you better be there to call them out and make the whole team now run with them." - "If you want to start the game 6-0, like I start most games just because my team is ready to go because we go so hard every day."

James Light

162,675 views • 5 months ago

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Roan

119,605 views • 5 days ago

Trying Gemini today It feels like Google is trying to scare customers into NOT using it with legal popups literally every step of the way Admittedly it's gotten better and now supports CURL requests without all the Google libraries to install But still no OpenAI-compatible queries yet Took 9 clicks to get an API key, and the legal popups are just way too crazy If Logan Kilpatrick can just get all the lawyers of his back, it could be great

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@levelsio

413,444 views • 1 year ago

If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

Ruben Hassid

167,306 views • 4 days ago

fun weekend project: AutoJev. i was curious to see if i could train a competitive Jev-like model completely autonomously with a swarm of agents using our internal system. turns out you can get quite far! some details: - gave the swarm a devbox with an h200 gpu - the swarm is a mix of astra and fable; used sol and luna for synthetic data and filtering; ran for 20 hours - spent $3.1k in total ($1.9k on agents, $1.2k on data) - used qwen3.8-27b as the backbone. it underperforms Jev initially, but after training it is competitive/better, and it also supports 260k context and multimodal input - the agents tried RL, but it hasn't worked out so far, so they fell back to SFT on high-quality synthetic data - both accuracy and calibration are better - jev-compatible api, playground, and inference. on an h200 it gets around 120ms p95. that will be higher under contention, but still quite fast for an unoptimized version code: model: going to let it run longer, and will also bring up inference so folks can try it out.

fun weekend project: AutoJev. i was curious to see if i could train a competitive Jev-like model completely autonomously with a swarm of agents using our internal system. turns out you can get quite far! some details: - gave the swarm a devbox with an h200 gpu - the swarm is a mix of astra and fable; used sol and luna for synthetic data and filtering; ran for 20 hours - spent $3.1k in total ($1.9k on agents, $1.2k on data) - used qwen3.8-27b as the backbone. it underperforms Jev initially, but after training it is competitive/better, and it also supports 260k context and multimodal input - the agents tried RL, but it hasn't worked out so far, so they fell back to SFT on high-quality synthetic data - both accuracy and calibration are better - jev-compatible api, playground, and inference. on an h200 it gets around 120ms p95. that will be higher under contention, but still quite fast for an unoptimized version code: model: going to let it run longer, and will also bring up inference so folks can try it out.

Denis Yarats

27,046 views • 3 days ago

Jev is a new model from the co-inventor of ChatGPT that can use computer tools in realtime right now it's playing Minecraft on its own computer in the cloud on Orgo this is what it looks like with just one Jev, but imagine: what about a thousand? or what about a billion of Jevs? each working together on their own computers in the cloud, discovering new science, running experiments, and designing new drugs 24/7. this is going to be bigger than ChatGPT

Jev is a new model from the co-inventor of ChatGPT that can use computer tools in realtime right now it's playing Minecraft on its own computer in the cloud on Orgo this is what it looks like with just one Jev, but imagine: what about a thousand? or what about a billion of Jevs? each working together on their own computers in the cloud, discovering new science, running experiments, and designing new drugs 24/7. this is going to be bigger than ChatGPT

Nick Vasilescu

16,613 views • 8 days ago