Tether Introduces AI Framework For Consumer GPUs and Smartphones

Tether launches AI framework for smartphones and consumer GPUs, enabling fast, low-memory model training without cloud or NVIDIA hardware.

Tether has introduced a new AI training framework designed for consumer devices. The system supports smartphones and a wide range of GPUs, including non-Nvidia hardware.

The release marks a step toward making AI training more accessible and less dependent on large data centers.

Framework Designed for Consumer Hardware

Tether announced that the framework is part of its QVAC platform. which enables advanced

AI models to run directly on smartphones and laptops without data centers or expensive hardware.

It supports iPhone, Android, and desktops, uses up to 90 percent less memory, and delivers faster performance without relying on NVIDIA GPUs or the cloud, bringing AI closer to personal devices.

The company stated that the system uses Microsoft’s BitNet architecture and LoRA methods.

These tools reduce memory use and computing demand. This approach lowers hardware costs and expands access to AI development.

Tether said, “The framework supports cross-platform training and inference across multiple chipsets.”

These include AMD, Intel, and Apple Silicon. It also supports mobile GPUs from Qualcomm and Apple.

Performance and Efficiency Gains

The framework uses a 1-bit model design based on BitNet. Tether reported that this can reduce VRAM use by up to 77.8%.

This allows larger models to run on devices with limited resources. Engineers at Tether tested the system on smartphones.

They fine-tuned models with up to one billion parameters in under two hours. Smaller models required only minutes to train.

The company also reported support for models as large as 13 billion parameters on mobile devices. In addition, mobile GPUs showed faster performance than CPUs during inference tasks.

The framework enables LoRA fine-tuning on non-Nvidia hardware. This expands compatibility beyond traditional AI training systems.

It also supports distributed learning methods across multiple devices.

Related Reading: Tether Eyes $500B Valuation With New USAT Stablecoin Launch

Growing Link Between Crypto and AI

Tether’s move comes as crypto firms expand into AI and computing services. Many companies are investing in infrastructure that supports machine learning workloads.

Recent industry activity shows increased funding and partnerships. Google acquired a stake in Cipher Mining as part of a long-term data center deal.

Other firms are also raising funds to support AI operations. At the same time, AI agents are gaining use across blockchain platforms.

These programs can perform tasks and interact with services independently. Companies are building tools that connect AI systems with crypto networks.

Tether said its framework can support on-device training and federated learning. This allows data to stay on local devices while models improve. It reduces reliance on centralized cloud systems and supports broader deployment.

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