The article also highlights the potential applications of LLMs on the edge, such as in defense, finance, and remote resource operations, where data security and connectivity are crucial. Tensorizing neural networks allows for rapid and repeated running of models on local devices without risking data privacy or requiring constant server connection. Despite being a new technique, tensorizing neural networks offers a promising solution for enterprises to reduce costs, maintain data privacy, and utilize LLMs on the edge until fully fault-tolerant quantum computing hardware becomes available.
Key takeaways:
- Large language models (LLMs) are becoming prohibitively expensive to train, limiting their accessibility to only the biggest enterprises, and cloud-hosted LLMs introduce concerns such as privacy and data sovereignty.
- Quantum-inspired algorithms, specifically tensorizing neural networks, can resolve many of the issues constraining the use of machine learning on local devices.
- Tensorizing neural networks reduces redundancy in the data structure, resulting in a smaller model that can be trained faster on classical computers without the need for more hardware.
- Localized training of LLMs using tensor networks can benefit sectors such as defense, finance, and remote resource operations, where data security, speed, and connectivity are critical.