Hyper.Train is a quantum-inspired algorithm aimed at optimizing large language models (LLMs) on AI infrastructure. The proprietary methods embedded in Hyper.Train promise to reduce the total cost of ownership for AI compute by at least 30%.
Hyper.Train employs three patented methods. Critical Node Detection, Polymorphic Pruning with quantum-inspired optimization, and Critical Neuron Selection. These aim to enhance LLMs by removing redundancy, optimizing connections between neurons, and reordering neurons.
Hyper Intelligence is the second spin-out of Entanglement Inc. and is a software-only and hardware-agnostic technology company revolutionizing the explosive GenAI and LLM market.
By using this site, you agree to allow SPEEDA Edge and our partners to use cookies for analytics and personalization. Visit our privacy policy for more information about our data collection practices.