SubQ

A new LLM architecture called SubQ achieves fully subquadratic attention, reducing computational cost from O(n²) to O(n log n). This breakthrough eliminates the scaling bottleneck that forces models t…

Subquadratic, a Miami AI startup, claims to have broken through the transformer attention bottleneck that limits large language models, introducing Subquadratic Sparse Attention (SSA) to enable effici…

Miami startup Subquadratic claims to have solved a decade-old bottleneck in AI models by replacing dense attention with sparse attention, achieving 56x speed gains and dramatic cost reductions. Indepe…

Subquadratic founders Justin Dangel and Alex Whedon released third-party benchmarks from Appen to support their claim that SubQ, a sparse-attention LLM, outperforms dense transformers on long-context …

Miami-based AI startup Subquadratic claims to have solved a mathematical bottleneck limiting large language models, introducing SubQ, a faster, cheaper, and more energy-efficient model that processes …

Subquadratic launched SubQ, a sub-quadratic sparse-attention LLM with a 12-million-token context window, enabling multi-million token reasoning at linear cost. The model reduces attention compute by n…

SubQ, a new large language model built on a subquadratic sparse attention architecture, has entered private beta with a 12-million-token context window. The architecture allows compute to scale linear…