Tether Data has launched QVAC Fabric LLM, a groundbreaking framework that brings the power of large language models (LLMs) directly to consumer hardware like smartphones and laptops. This marks a major shift in AI development by making LLM inference and fine-tuning possible on everyday devices. Previously restricted to expensive cloud servers or specialized hardware, QVAC Fabric empowers developers and organizations to build, deploy, execute, and customize AI privately and independently, eliminating the need for cloud dependency and vendor lock-in.
QVAC Fabric’s key advancements include:
* **High-Performance Inference:** The framework enables LLM inference execution, LoRA, and instruction-tuning on mobile devices (iOS and Android), laptops, desktops, and servers, providing unparalleled flexibility.
* **Fine-Tuning on Mobile GPUs:** This innovation allows for the first-time production-ready training of models on smartphone-class hardware like Qualcomm Adreno and ARM Mali.
* **Enhanced Llama Ecosystem:** QVAC Fabric expands the llama.cpp ecosystem with support for modern models like LLama3, Qwen3, and Gemma3.
QVAC Fabric eliminates the need for specialized hardware by offering a universal platform that empowers users to build AI tailored to their specific needs and environments.
This breakthrough opens up opportunities for businesses to fine-tune AI models in-house, ensuring data privacy and regulatory compliance while deploying powerful AI solutions.
Paolo Ardoino, CEO of Tether, states: “AI should not be controlled only by large cloud platforms. QVAC Fabric LLM gives people the ability to execute inference and fine-tune powerful models on their own terms, on their own hardware with full control of their data.” This marks a crucial step towards decentralized AI development that prioritizes privacy, resilience, and local control.
QVAC Fabric is available as open-source software under the Apache 2.0 license, alongside multi-platform binaries and ready-to-use adapters on Hugging Face. Developers can now begin fine-tuning with ease using a simple workflow.
This groundbreaking technology promises to reshape the future of AI by enabling a more accessible, resilient, and decentralized approach to intelligent systems that are no longer limited by physical constraints or reliance on cloud infrastructures.