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 released the model card and technical report for SubQ 1.1 Small, a long-context model using Subquadratic Sparse Attention, on June 16. The company claims the model achieves 100% retrieval…