#memory optimization

PARSCALE introduces a parallel computation approach to scale language models efficiently, reducing memory use and latency while improving performance across various tasks.

Researchers from Edinburgh, Cohere, and Meta demonstrate that large sparse models can outperform smaller dense models for long-context LLMs by leveraging sparse attention, offering new scaling laws and standardized methods.