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QVAC is an open-source, cross-platform ecosystem for building local-first, peer-to-peer AI applications and systems. With QVAC, you can run AI tasks like LLMs, speech, RAG, and more locally across Linux, macOS, Windows, Android, and iOS — or delegate inference to peers using its built-in P2P capabilities.

Key features

• Local-first: load AI models and perform inference on your own machine. No third-party APIs, SaaS, or cloud involved.
• P2P: build unstoppable internet systems — like BitTorrent, IPFS, and blockchain networks, but for AI.
• Cross-platform: consistent developer experience across hardware, operating systems, and JS runtime environments — write code once, run it everywhere.
• OpenAI-compatible API: integrate with the broader AI ecosystem.
• Open source: 100% free to use and modify — build on top, contribute back, be part of our community.

Usage

QVAC is composed of JavaScript libraries and tools that converge in the JS SDK. The SDK is the main entry point for using QVAC. It is type-safe and exposes all QVAC capabilities through a unified interface. It runs on Node.js, Bare runtime, and Expo.

Additionally, QVAC also provides an HTTP server, allowing you to use it as a local model provider for your favorite AI tools, such as OpenCode, OpenClaw, and any other application compatible with the OpenAI API.

Whether you're building applications with the SDK or using QVAC as a local model provider, the principle is the same: load models and run inference locally, or delegate inference to peers using the built-in P2P capabilities.

AI capabilities

Task	Description
Text generation	LLM inference for text generation and chat via Fabric LLM.
Text embeddings	Vector embedding generation for semantic search, clustering, and retrieval, via Fabric LLM.
RAG	Out-of-the-box retrieval-augmented generation workflow.
Fine-tuning	Adapting LLMs to domain-specific tasks via LoRA.
Multimodal	LLM inference over text, images, and other media within a single conversation context.
Image generation	Text-to-image and image-to-image generation via a customized Diffusion backend.
Video generation	Text-to-video and image-to-video generation via a customized Diffusion backend.
Transcription	Automatic speech recognition (ASR) via a customized Whisper backend or NVIDIA Parakeet.
Text-to-Speech	Speech synthesis (TTS) via a customized GGML backend.
Translation	Text-to-text neural machine translation (NMT), via Fabric LLM and Bergamot.
BCI	Brain–computer interface transcription via a customized Whisper backend.
VLA	Vision-language-action for robot control via a customized GGML backend.
OCR	Optical character recognition for extracting text from images via ONNX Runtime or GGML backends.
Image classification	Classify images into labels with confidence scores via a customized GGML backend.

Resources

Explore and use QVAC:

Resource	Description
Docs	Comprehensive QVAC documentation.
Examples	Sample apps and PoCs built with QVAC SDK.
Local model provider	Use QVAC as a local model provider connected to your favorite AI tools.
QV.AC	Get to know our local AI assistant.
Support and community	We gather on Discord and Keet. Ask for help, give feedback, and discuss QVAC.
Blog	Tutorials, deep dives, engineering notes, and announcements.
Ecosystem	Discover the broader QVAC ecosystem.
Research	Papers, datasets, and models optimized for edge devices.
Our vision	Learn why Tether built QVAC.

Note: access our Keet room via this link:

keet://chat/nfo61f4e...

keet://chat/nfo61f4e6zc5t1ifncyh9yp7s5eynbruz5bs95oc5ufn3e79entmhix74miigc8iz9iawfrb7pzk3am8eotxw8wat7554etbn7d6j4ho84b1zqnb63z7hxq1ubt5w4wi4kpq3mdgpijcnaifnhm7sy4cfxqqoyedpnb5qg1majcggy4s9s91fgtg3khgw

Quickstart

Want to get hands-on right away? Here's a simple example you can use to test QVAC.

1. Create the examples workspace:

mkdir qvac-examples
cd qvac-examples
npm init -y && npm pkg set type=module

2. Install the SDK:

npm install @qvac/sdk

3. Create the quickstart script:

import { loadModel, LLAMA_3_2_1B_INST_Q4_0, completion, unloadModel, } from "@qvac/sdk";
try {
    // Load a model into memory
    const modelId = await loadModel({
        modelSrc: LLAMA_3_2_1B_INST_Q4_0,
        modelType: "llm",
        onProgress: (progress) => {
            console.log(progress);
        },
    });
    // You can use the loaded model multiple times
    const history = [
        {
            role: "user",
            content: "Explain quantum computing in one sentence",
        },
    ];
    const result = completion({ modelId, history, stream: true });
    for await (const token of result.tokenStream) {
        process.stdout.write(token);
    }
    // Unload model to free up system resources
    await unloadModel({ modelId });
}
catch (error) {
    console.error("❌ Error:", error);
    process.exit(1);
}

4. Run the quickstart script:

node quickstart.js

You'll see the model download first. Then, QVAC will stream the response tokens and print them to the terminal.

Contributing

We welcome contributions! Feel free to open a pull request, report bugs, or share ideas through issues.

See CONTRIBUTING for details.

Banners and badges

Built something with QVAC? Add a badge or banner to your README, website, or app. It is a simple way to highlight your project, help others discover QVAC, and strengthen our community.

By using these badges and banners, you help foster the QVAC ecosystem!

Choose a banner or badge below and copy its Markdown snippet, or copy its image URL and use the hosted SVG asset directly.

Banners

Large format badges (240x60) for prominent placement in your README header.

Dark with monochrome glow

Dark with colorful flow

Dark with stars pattern

Light with colorful flow

Banner usage

[![Built with QVAC](https://raw.githubusercontent.com/tetherto/qvac/refs/heads/main/docs/branding/qvac-banner-dark-glow.svg)](https://github.com/tetherto/qvac)

Badges

Compact badges for use alongside other shields/badges in your README.

Compact

Variant	Dark bg	Light bg
Green logo
Monochrome

Inline

Variant	Dark bg	Light bg
Green logo
Monochrome

Badge usage

[![Built with QVAC](https://raw.githubusercontent.com/tetherto/qvac/refs/heads/main/docs/branding/qvac-badge-green-dark.svg)](https://github.com/tetherto/qvac)