Robots need their intelligence on board, not in a data center. @G_Bionics chose QVAC for theirs. What this new demo, presented by Marco, Head of QVAC, and Pietro, AI architect at Generative Bionics.

Picture a world where every phone, every laptop, every device runs its own intelligence. Not a thin client connected to a brain in a data center. Its own. No subscription. No account. No request leaving the device. The intelligence is just there, and it belongs to you. Yours to own, the way the device itself is, not a service you log into. It keeps working on a plane, in a blackout, in a place the cloud never reached. It speaks the language of the person holding it, not only the languages that were profitable to support. This is not a smaller copy of cloud AI. It is a different shape entirely: distributed, owned, resilient, with no single point that can fail or be switched off. Intelligence as infrastructure for everyone, not a utility metered by a few. That is the QVAC thesis.

If you are in Tbilisi on August 7: come and get a model running locally on your own machine, in one evening. Hands-on with QVAC for on-device AI, @pears_p2p for P2P apps, @WDK_tether for wallets. 18:00. RSVP:

Today we release VisionPsy-Nano: state-of-the-art vision-language models at 460M parameters, built to run on-device. It leads every model in its weight class on 16 of 17 benchmarks with the highest overall normalized score in its class, and it tops all four capability areas: document understanding and OCR, visual perception, reasoning, and instruction following. Open weights, Apache 2.0.

Introducing VisionPsy-Nano: state-of-the-art vision-language models at 460M parameters, small enough to run on the phone in your pocket. VisionPsy-Nano-460M leads every other ~0.5B vision-language model tested & compares favourably on 16 of 17 benchmarks, tops all four capability categories, and outperforms models up to 2.3x larger. Two variants, one tuned for maximum accuracy and one for real-time responsiveness. Open weights, Apache 2.0, and no cloud in the loop.

@thisonedevv built a code school for our stack: around 70 QVAC samples, runnable on your own machine, fully offline, with instant feedback on each lesson. Good to see the community growing the ecosystem!

QVAC SDK 0.16 is live. 🚀 - Ideogram 4 image generation, on-device - LTX-2 video with synchronized audio - NVIDIA's GR00T robotics model, running local - OpenAI compatible server improvements for better coding agents integration - Speech packages improvements and TypeScript-native migration Full release notes:

The mechanic behind every story like this: the conversation lives on a server, so it can be linked, crawled, and indexed. On-device AI removes the first step. No server copy, no URL, nothing to index. Privacy you do not have to configure.

Your laptop's AI forgets long conversations because it runs out of working memory, not intelligence. QVAC team shipped an open-source fix: TurboQuant, a 5x memory compression algorithm for local AI, available in QVAC SDK. Read how it works:

Have you ever tried building with the QVAC SDK? This dev just shipped a fully functional local AI second brain running entirely on QVAC, weekend build, open source. This is exactly the kind of app the SDK is for.

Physical AI will run on billions of devices. Centralized infra means someone else controls the robot in your house. Local-first changes that equation, same logic we build into QVAC. Full piece on why decentralization is the safety requirement, not a nice-to-have:

"Chat with your documents" usually means uploading them to someone else's cloud. The QVAC SDK does the whole thing on-device. It ships native RAG: ragIngest your files, embed turns them into vectors locally, ragSearch finds the relevant passages, and completion answers from text the model can actually see. No external vector database. What you can build with it: a knowledge base over years of your notes, contract and research Q&A, a support bot grounded only in your manuals, a private second brain that cites its sources. Your private documents never leave the machine. No cloud vector store, no API key, no leak surface. npm install @qvac/sdk

Cloud AI forces users to rent intelligence from centralized, high-cost data centers. Tether is breaking this model with local, edge-first AI. By enabling on-device inference and training on everyday hardware, AI becomes a portable capital asset owned entirely by you. Read more:

Generative images and video, with no cloud bill and no content filter you do not control. The QVAC SDK runs both on-device. diffusion runs FLUX.2-klein and Stable Diffusion locally, blends reference images for style, and upscale takes the result to high resolution. And video generates short clips from a text prompt, all on the device. What you can build with it: avatar and sticker makers, design and mockup tools, wallpaper and art generators, short animated clips and video effects, character creators. Every image and frame is free and private. No per-image cost, nothing uploaded, works offline. npm install @qvac/sdk

50+ languages. Zero servers. QVAC now powers fully on-device translation inside @keet_io. Every word is translated locally. Nothing sent anywhere. No one listening. Test it on - the chat app that knows nothing about you.

Rome. Le Corsie Sistine. The Last Question. @paoloardoino took the stage to make the case for local AI over centralized AI and for putting intelligence back in the hands of the people who use it. Not your AI, not your intelligence. #TheLastQuestion# #QVAC#

QVAC is built by a team working at the frontier where AI, biology, and computing converge, a convergence that is compressing decades of progress into years. That means real research, in the open. Tether just had four papers accepted at ICLR, and open-sources the whole stack for millions of developers and researchers to scrutinize.