---
title: 'Mamba: Linear-Time Sequence Modeling with Selective State Spaces'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2312-00752-mamba.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Albert Gu, Tri Dao, Mamba: Linear-Time Sequence Modeling with Selective State Spaces (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked; code not run.'
category: arxiv
topic_area: 'ai-research: memory-and-context'
tags: arxiv, sequence-models, state-spaces
arxiv_id: 2312.00752
alias: 2312.00752-mamba
sources:
  - id: S1
    title: 'Mamba: Linear-Time Sequence Modeling with Selective State Spaces'
    publisher: arXiv
    authors: 'Albert Gu, Tri Dao'
    url: https://arxiv.org/abs/2312.00752
    version_read: 'v2 (31 May 2024)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# Mamba: Linear-Time Sequence Modeling with Selective State Spaces

## Summary
Mamba is a sequence model based on selective state space models (SSMs). The authors let SSM parameters depend on the input so the model can selectively propagate or forget information, and they describe a hardware-aware parallel algorithm in recurrent mode. They report 5 times higher inference throughput than Transformers, linear scaling in sequence length, and improving performance on real data up to million-length sequences. On language modeling, they say Mamba-3B outperforms same-size Transformers and matches models twice its size. [S1]

## Claims
- id: C1
  claim: 'Mamba integrates selective SSMs into a simplified architecture without attention or even MLP blocks.'
  source: S1
  evidence: "without attention or even MLP blocks (Mamba)"
- id: C2
  claim: 'They report 5 times higher throughput than Transformers and linear scaling in sequence length.'
  source: S1
  evidence: "5× higher throughput than Transformers"
- id: C3
  claim: 'They say Mamba-3B outperforms same-size Transformers and matches models twice its size.'
  source: S1
  evidence: "our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size"

## Verification
- Code available: A public implementation is widely associated with https://github.com/state-spaces/mamba, which returned HTTP 200 on 2026-11-11. The note records that URL after it appeared in the HTML; code was not run.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live notes 2309-17453-streamingllm-attention-sinks and 2404-06654-ruler. This is the paper-verification note for Mamba itself.
- Limits: Throughput and quality claims are the authors' reported results in the v2 paper (revised 31 May 2024). The GitHub URL was present in the HTML; code was not run.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 1 Dec 2023 (v1); last revised 31 May 2024 (v2). Sources: [S1] https://arxiv.org/abs/2312.00752, v2 (31 May 2024). checked_on: 2026-11-11.

## When to apply
Use when comparing linear-time sequence models with attention for long context, or when reading later selective SSM work.

## Facts
- id: F1
  claim: 'Mamba is a selective state-space sequence model that can propagate or forget information based on the current token.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors report 5 times higher inference throughput than Transformers and linear scaling in sequence length.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They report that Mamba-3B outperforms same-size Transformers and matches Transformers twice its size on language modeling.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2312.00752, https://arxiv.org/abs/2312.00752. License: CC BY 4.0. v2 (31 May 2024). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).