QVAC SDK, a universal JavaScript SDK for building local AI applications
Hi folks, today we're launching QVAC SDK [0], a universal JavaScript/TypeScript SDK for building local AI applications across desktop and mobile. The project is fully open source under the Apache 2.0 license. Our goal is to make it easier for developers to build useful local-first AI apps without having to stitch together a lot of different engines, runtimes, and platform-specific integrations. Under the hood, the SDK is built on top of QVAC Fabric [1], our cross-platform inference and fine-tuning engine. QVAC SDK uses Bare [2], a lightweight cross-platform JavaScript runtime that is…
In plain words
QVAC SDK is an open-source JavaScript and TypeScript framework for developers building local AI applications on desktop and mobile platforms. Built on the QVAC Fabric inference engine, it simplifies development by consolidating multiple engines and runtimes into a single SDK. The framework supports various AI tasks including language models, OCR, translation, transcription, and text-to-speech, running locally across Node, Bun, and React Native environments.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi folks, today we're launching QVAC SDK [0], a universal JavaScript/TypeScript SDK for building local AI applications across desktop and mobile. The project is fully open source under the Apache 2.0 license. Our goal is to make it easier for developers to build useful local-first AI apps without having to stitch together a lot of different engines, runtimes, and platform-specific integrations. Under the hood, the SDK is built on top of QVAC Fabric [1], our cross-platform inference and fine-tuning engine. QVAC SDK uses Bare [2], a lightweight cross-platform JavaScript runtime that is part of the Pear ecosystem [3]. It can be used as a worker pretty much anywhere, with built-in tooling for Node, Bun and React Native (Hermes). A few things it supports today: - Local inference across desktop, mobile and servers - Support for LLMs, OCR, translation, transcription, text-to-speech, and vision models - Peer-to-peer model distribution over the Holepunch stack [4], in a way that is similar to BitTorrent, where anyone can become a seeder - Plugin-based architecture, so new engines and model types can be added easily - Fully peer-to-peer delegated inference We also put a lot of effort into documentation [5]. The docs are structured to be readable by both humans and AI coding tools, so in practice you can often get pretty far with your favorite coding assistant very quickly. A few things we know still need work: - Bundle sizes are larger than we want right now because the current packaging of Bare add-ons is not as efficient as it should be yet - Plugin workflow can be simpler - Tree-shaking is already possible, but at the moment it still requires a CLI step, and we'd like to make that more automatic and better integrated into the build process This launch is only the beginning. We want to help people build local AI at a much larger scale. Any feedback is truly appreciated! Full vision is available on the official website [6]. References: [0] SDK: http://qvac.tether.io/dev/sdk [1] QVAC Fabric: https://github.com/tetherto/qvac-fabric-llm.cpp [2] Bare: https://bare.pears.com [3] Pear Runtime: https://pears.com [4] Holepunch: https://holepunch.to [5] Docs: https://docs.qvac.tether.io [6] Website: https://qvac.tether.io
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