{"slug": "subq-the-first-fully-subquadratic-llm-cost-comparison-with-transformers-2026", "title": "SubQ: The First Fully Subquadratic LLM — Cost Comparison with Transformers [2026]", "summary": "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 to break with long inputs, potentially replacing RAG and chunking for large-context tasks.", "body_md": "There is a fundamental problem with all the large language models you use today — and it has to do with high school math.\n\nThe heart of the Transformer is **attention**: each token needs to compare itself with every other token. A text of 1,000 words requires 1 million comparisons (1,000²). A text of 1 million tokens requires **1 trillion comparisons**. The computational cost grows with the **square of the context** — O(n²). It is the reason why models “break” with very long inputs, why we use RAG, chunking, and agents instead of simply giving the entire document to the model.", "url": "https://wpnews.pro/news/subq-the-first-fully-subquadratic-llm-cost-comparison-with-transformers-2026", "canonical_source": "https://www.lucasaguiar.xyz/posts/subq-subquadratic-llm-atencao-linear-comparacao-custos-2026/", "published_at": "2026-07-07 21:00:00+00:00", "updated_at": "2026-07-08 01:39:16.496309+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-infrastructure"], "entities": ["SubQ", "Transformer"], "alternates": {"html": "https://wpnews.pro/news/subq-the-first-fully-subquadratic-llm-cost-comparison-with-transformers-2026", "markdown": "https://wpnews.pro/news/subq-the-first-fully-subquadratic-llm-cost-comparison-with-transformers-2026.md", "text": "https://wpnews.pro/news/subq-the-first-fully-subquadratic-llm-cost-comparison-with-transformers-2026.txt", "jsonld": "https://wpnews.pro/news/subq-the-first-fully-subquadratic-llm-cost-comparison-with-transformers-2026.jsonld"}}