Subquadratic claims to break LLM scaling limits! 1000x less costs

Subquadratic, founded by ex-DeepMind and Meta engineers, claims a linear-scaling architecture achieving 52x efficiency gains at 1M tokens versus standard Transformers, with a reported 12M-token context window and up to 1000x cost reduction. If validated, this directly challenges quadratic attention complexity that bottlenecks current LLMs. RAG pipelines and long-context inference workloads stand to benefit most, potentially rendering chunking strategies obsolete. Early access only via waitlist at subq.ai, no peer-reviewed benchmarks published yet.