--- title: "QVAC by Tether: the Infinite Stable Intelligence Platform" canonical: https://docs.qvac.tether.io/ecosystem/ collection: "Ecosystem" --- # QVAC by Tether: the Infinite Stable Intelligence Platform (/ecosystem) import { MessagesSquare, Hash, Languages, Mic, Music, Speech, Volume2, ScanText, Image as ImageIcon, Video, Rotate3d, GalleryHorizontal, FlaskConical, ScanSearch, Shapes, Eye, Brain, DoorOpen, Server, MonitorPlay } from 'lucide-react' import { SiKotlin, SiPython, SiTypescript } from '@icons-pack/react-simple-icons' ## Why QVAC? QVAC is Tether's answer to centralized AI by ensuring AI is not tied to massive data centers in the hands of a few *but is free to run on everyone's devices, without a central point of failure or arbitrary censorship*. ## Features ## AI capabilities 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.

World simulation}> Turn a first-frame image and navigation controls into a streamed, navigable video via an interactive world model. Music generation}> Generate music from text prompts and lyrics via ACE-Step or MiniMax-Music3. Transcription}> Automatic speech recognition (ASR) via a customized Whisper backend or NVIDIA Parakeet. Text-to-Speech}> Speech synthesis (TTS) via a customized GGML backend. Voice assistant}> Real-time voice pipeline: transcription, text generation, and speech synthesis in one loop. 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. Image classification}> Classify images into labels with confidence scores via a customized GGML backend. ## System overview

*The SDK is the main entry point for using QVAC*. It is type-safe and exposes all QVAC capabilities through a unified interface. Two clients share that interface — JS/TS (`@qvac/sdk`, running on Node.js, [Bare](https://bare.pears.com), and [Expo](https://expo.dev)) and Python (`tetherto-qvac-sdk`, asyncio-native) — and both call into the same underlying runtime, so capabilities and behavior are identical either way. Additionally, QVAC provides a CLI with development tools and an HTTP server that wraps QVAC and exposes an [**OpenAI-compatible API**](https://ecosystem.openai.com/docs/api-reference). *You can use the HTTP server as a local model provider to connect any AI tool, harness, or system.* Finally, QVAC also encompasses [**QV.AC**](https://qv.ac), a desktop and mobile assistant app, to empower users and showcase QVAC capabilities, as well as [**research initiatives**](https://huggingface.co/qvac) to advance the state of the art in local AI. ## Next steps Choose how you want to start with QVAC: } title="Get started"> Learn the essentials for using the SDK: system requirements, compatibility matrix, setup basics, and core usage flows. } title="JS/TS SDK"> Install `@qvac/sdk`, run the quickstart, and target Node.js, Bare, or Expo. } title="Python SDK"> Install `tetherto-qvac-sdk`, run the quickstart, and drive the same worker from Python. } title="See QVAC in practice"> Try QV.AC, our flagship desktop and mobile assistant app built with QVAC. } title="Use QVAC as a local model provider"> Run the QVAC HTTP server and connect it to any OpenAI-compatible tool, harness, or system.