{"data":{"slug":"mamba","etype":"concept","name":"State-Space Models (Mamba)","cat":"Core Technical","subtitle":"The main architectural challenger: linear-time sequence processing.","summary":"State-space models like Mamba process sequences in linear time with constant memory — no quadratic attention, no ballooning KV cache. Pure SSMs trail transformers on recall-heavy tasks, so production systems hybridize (AI21's Jamba, MiniMax's lightning attention). If context keeps growing, the efficiency argument keeps getting stronger — one of the live architecture debates that could reshape hardware demand.","attrs":null,"kf":null,"aliases":["SSM","state space models"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://arxiv.org/abs/2312.00752","label":"Mamba paper"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T09:49:55+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}