Compressing LLMs With Quantum-Inspired Software

Quantum-inspired software is emerging to enhance LLM efficiency. Multiverse Computing's techniques compress LLMs, reducing memory by 93% and parameters by 70% for models like LlaMA-2-7B. This accelerates training by 50% and inference by 25%, with minimal accuracy loss. The technology addresses high costs and energy consumption of LLMs, which face efficiency challenges at scale. These advancements show promise for more sustainable and accessible AI, though not yet at quantum computing levels.