QuarterBit – Train 70B LLMs on a single GPU (quarterbit.dev)

QuarterBit has unveiled AXIOM, a groundbreaking technology that enables the training of 70 billion parameter models on a single GPU, drastically slashing energy consumption by 91% and costs by 90%. This innovation addresses the ever-increasing power demands of data centers and the lengthy waitlists for GPUs, providing a viable solution for researchers and organizations striving to scale their AI capabilities without the need for extensive hardware upgrades. AXIOM supports any PyTorch or HuggingFace model, making frontier-scale model training accessible to a broader audience. This significant advancement allows users to train larger models that previously required multiple GPUs using just one, unlocking potential for various applications across different domains, including LLMs, computer vision, and audio processing. By compressing the training state rather than modifying the models themselves, AXIOM retains the ability to update weights throughout training, ensuring high-quality results without a loss in learning. Users can simply install AXIOM via pip, paving the way for efficient, cost-effective AI development without the need for complex infrastructure adjustments.