Tether Data Unveils QVAC Fabric LLM Inference And Fine-Tuning Framework For Modern AI Models
In Brief
Tether Data has launched QVAC Fabric LLM framework that enables LLM inference and fine-tuning across consumer devices and multi-vendor hardware, supporting decentralized, privacy-focused, and scalable AI development.
Department of Financial Services company Tether, focused on promoting freedom, transparency, and innovation through technology, Tether Data announced the launch of QVAC Fabric LLM, a comprehensive large language model (LLM) inference runtime and fine-tuning framework. This new system enables users to execute, train, and customize large language models directly on standard hardware, including consumer GPUs, laptops, and even smartphones, removing the previous dependence on high-end cloud servers or specialized NVIDIA setups.
QVAC Fabric LLM redefines high-performance LLM inference and fine-tuning, which were traditionally accessible only to organizations with expensive infrastructure. It represents the first unified, portable, and highly scalable system capable of full LLM inference execution, LoRA adaptation, and instruction-tuning across mobile operating systems (iOS and Android), as well as all common laptop, desktop, and server environments (Windows, macOS, Linux). This allows developers and organizations to build, deploy, run, and personalize AI independently, without reliance on the cloud, vendor lock-in, or the risk of sensitive data leaving the device.
A notable innovation in this release is the ability to fine-tune models on mobile GPUs, such as Qualcomm Adreno and ARM Mali, marking the first production-ready framework to enable modern LLM training on smartphone-class hardware. This advancement facilitates personalized AI that can learn directly from users on their devices, preserving privacy, operating offline, and supporting a new generation of resilient, on-device AI applications.
QVAC Fabric LLM also extends the llama.cpp ecosystem by adding fine-tuning support for contemporary models such as LLama3, Qwen3, and Gemma3, which were previously unsupported. These models can now be fine-tuned through a consistent, straightforward workflow across all hardware platforms.
By enabling training on a broad spectrum of GPUs, including AMD, Intel, NVIDIA, Apple Silicon, and mobile chips, QVAC Fabric LLM challenges the long-held notion that advanced AI development requires specialized, single-vendor hardware. Consumer GPUs are now viable for significant AI tasks, and mobile devices become legitimate training platforms, broadening the landscape for AI development.
For enterprises, the framework offers strategic advantages. Organizations can fine-tune AI models internally on secure hardware, eliminating the need to expose sensitive data to external cloud providers. This approach supports privacy, regulatory compliance, and cost efficiency while allowing deployment of AI models customized for internal requirements. QVAC Fabric LLM shifts fine-tuning from centralized GPU clusters to the broader ecosystem of devices already managed by companies, making advanced AI more accessible and secure.
Tether Data Releases QVAC Fabric LLM As Open-Source, Enabling Decentralized AI Customization
Tether Data has made QVAC Fabric LLM available as open-source software under the Apache 2.0 license, accompanied by multi-platform binaries and ready-to-use adapters on Hugging Face. The framework allows developers to begin fine-tuning models with just a few commands, reducing barriers to AI customization that were previously difficult to overcome.
QVAC Fabric LLM marks a practical move toward decentralized, user-managed AI. While much of the industry continues to prioritize cloud-based solutions, Tether Data focuses on enabling advanced personalization directly on local edge hardware. This approach supports operational continuity in regions with high-latency networks, such as emerging markets, while offering a privacy-first, resilient, and scalable AI platform capable of functioning independently from centralized infrastructure.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
