SubQ Launches: First Sub-Quadratic LLM With 12M-Token Context Window, $29M Seed Round

Subquadratic / Hacker News·medium signal

Miami-based Subquadratic emerged from stealth with $29M in seed funding and SubQ, a closed-weight LLM using Subquadratic Sparse Attention (SSA) that scales linearly with context length. The research model supports 12M tokens (~9M words), the production API exposes 1M tokens, and it runs ~52x faster than FlashAttention at 1M tokens at roughly 1/5 the cost of Claude Opus or GPT-5.5. Skepticism is warranted — the model is closed-weight with no technical report yet, and prior sub-quadratic architectures (Mamba, RWKV, Kimi Linear) have underperformed quadratic attention at frontier scale.