# Mamba Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/mamba
> Markdown URL: https://aitinkerers.org/technologies/mamba.md
> Technology record last updated: 2026-03-01T08:55:20Z
> Generated: 2026-10-25T13:51:12Z

Mamba is a Selective State Space Model (SSM) architecture that delivers Transformer-level quality with linear-time scaling ($O(L)$) and up to 5x faster inference throughput.

Mamba redefines sequence modeling: it's a Selective State Space Model (SSM) that introduces an input-dependent selection mechanism to filter information along the sequence length dimension (Source 1.2, 1.4). This design overcomes the limitations of prior SSMs, enabling content-based reasoning (Source 1.4). The result is a streamlined, attention-free architecture that achieves linear scaling in sequence length, dramatically improving efficiency for long-context data up to million-length sequences (Source 1.1, 1.4). Specifically, the Mamba-3B model matches the quality of Transformers twice its size and provides up to 5x higher generation throughput (Source 1.2, 1.4). It serves as a general-purpose backbone for diverse modalities: language, audio, and genomics (Source 1.1, 1.2).

- Official technology site: https://github.com/state-spaces/mamba
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Training Mamba for Long Context Sequences using Deepspeed](https://austin.aitinkerers.org/talks/rsvp_0KxbrCZJ164)

- Event context: Join AI Tinkerers April Meetup — 2024-04-11 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_0KxbrCZJ164

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