QVAC

️ 🖼Tool Name:

QVAC

🔖 Categories:

Automation and Smart Agents, Programming and Development, Integrations and APIs, Documents and Software Development Kits, Text and Images to Video, Text to Speech / Speech to Text, Image Design and Creation, Education and Research, Data Processing and Cleaning, Data Warehouses and Modeling

️ ✏What does this tool offer?

QVAC is an open-source, on-premises AI platform from Tether, designed to run AI models directly on the user’s own devices—such as phones, computers, and servers— rather than relying on centralized cloud servers.

The system allows developers to integrate on-device AI capabilities into applications via the QVAC SDK,so that models can be run and inferences made on the user’s device. The SDK provides a unified interface for a wide range of tasks, including text generation, embeddings, RAG, speech transcription, translation, image generation, text-to-speech, OCR, computer vision, image classification, image enhancement, and fine-tuning using LoRA.

QVAC operates on the principle of Local AI;inference is performed on the device, and applications do not need to send requests or files to a cloud service during operation. According to the official website, there are no API keys, per-token fees, or usage rate limits associated with the local inference itself.

QVAC supports running models on iOS, Android, macOS, Linux, and Windows;it also provides JavaScript/TypeScript and Python interfaces, and models can be loaded from a local file system, an HTTP URL, or QVAC’s distributed model registry.

The QVAC ecosystem consists of several components, most notably the QVAC SDK for application development,QVAC Models for local models,QVAC Fabric for fine-tuning, andQVAC Genesis for training resources and data, in addition to Q.AC Assistant, a helper application built on the QVAC platform.

QVAC also provides a local HTTP server that can serve the QVAC interface or an OpenAI API-compatible interface,allowing other tools and systems to connect to the local models running on the user’s device. (docs.qvac.tether.io)

⭐ What does it actually offer based on user experience?

• Running AI models locally on the device instead of sending data to the cloud.
• Build AI applications that run on phones, computers, and supported devices.
• Use a single API for text, speech, vision, and other tasks.
• Run LLM models locally for conversation, text generation, completion, and structured output.
• Create embeddings for semantic search and RAG applications.
• Implement RAG on local documents.
• Convert speech to text on-device.
• Translate text between languages offline.
• Generate images locally.
• Convert text to speech on-device.
• Extract text from images and documents using OCR.
• Analyze images using computer vision models.
• Classify images and return labels with a confidence score.
• Perform fine-tuning using LoRA, including on mobile devices.
• Run models from various sources, including QVAC models, model registries, local models, and GGUF from Hugging Face.
• Run QVAC as a local model provider for other tools via an HTTP server compatible with the OpenAI API.
• Run applications offline once the model is available on the device.
• Share models between devices through the P2P layer built into the SDK.

Official examples built using QVAC include: Tools for converting natural language to SQL, generating comics, real-time vision, invoice processing, smart cameras, and generating text, voice captions, and video translations, as well as examples for sports coaching and file analysis.

🤖 Does it include automation?

Yes, but QVAC is not a traditional RPA tool; it provides an infrastructure for executing AI tasks locally and automating functions within applications.

Developers can integrate functions such as speech recognition, translation, OCR, classification, text generation, RAG, and computer vision into application workflows through a unified SDK. QVAC can also run as a local server and connect to tools compatible with the OpenAI API.

The platform also supports P2P for exchanging or sharing models between devices without the need for a central server, and the SDK architecture includes a plugin system that allows developers to enable various AI capabilities or add custom features.

💰 Pricing Model:

QVAC is free and open source; it does not rely on a monthly subscription, per-code fees, or per-user inference fees when running models locally. The official website states that the system is free and open source and that the SDK is licensed under the Apache 2.0 license,with no API keys, per-code fees, or usage rate limits for local use.

The official website also confirms that the SDK is free even for commercial use, and that inference takes place on the user’s own devices.

🆓 Free Plan Details:

QVAC states that the SDK is free and open source under the Apache 2.0 license, and running models locally does not incur per-code or per-request fees. However, separate costs may apply if a developer uses third-party services or infrastructure that is not part of QVAC’s local deployment. (qvac.tether.io)

💳 Paid Plan Details:

QVAC does not currently offer a traditional paid plan such as Starter, Pro, or Enterprise for the SDK; the advertised base model is free and open source. Furthermore, the use of third-party services or additional infrastructure may result in separate costs, which should not be considered part of the QVAC pricing itself.

🧭 How to access the tool:

The QVAC documentation states that the JS/TS SDK runs on Node.js, Bare, and Expo, while the Python SDK is available with support for iOS and Android via Expo. The current compatibility matrix also indicates that the extensions support macOS 14+, iOS 17+, Ubuntu 22+, Android 12+, and Windows 10+, using Metal or Vulkan depending on the device and operating system.

🔗 Demo link or official website:

QVAC Official Website

QVAC SDK

Official QVAC documentation

QVAC repositories on GitHub