Mamba Explained: The LLM Architecture With No Attention (5x Faster, Million-Token Context)
Mamba replaces attention with a selective state space model — one fixed-size state updated per token with a learned keep-or-forget decision — reporting linear scaling and 5x Transformer inference throughput with quality holding to million-token sequences.
Where this fits in Mixpeek
Long-form video and multi-hour audio push any sequence model against attention's quadratic cost. Linear-scaling architectures like Mamba matter to Mixpeek wherever a model has to read a very long sequence — full-length transcripts, long documents, extended video — without the memory blowup that forces aggressive chunking. It informs how Mixpeek can process long modalities end-to-end rather than in lossy windows.
About this research
Transformers pay a quadratic tax: every new token re-reads the entire sequence, and the cache grows forever. Mamba replaces attention with a selective state space model: one fixed-size state updated per token, with a learned keep-or-forget decision. The paper reports linear scaling, 5x Transformer inference throughput, a 3B model matching Transformers twice its size, and quality holding up to million-token sequences. Paper: arxiv.org/abs/2312.00752
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Mixpeek turns video, images, audio, and documents in your object storage into searchable, timestamped results through one API — the retrieval stack these papers describe.
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