Samsung Labs claims its LittleBit latent factorization method achieves ultra-low-bit quantization at 0.1 BPW surpassing leading techniques at 0.7 BPW, potentially shifting the quality-size frontier for local LLM inference.

state: watchingheat: mediumuncertainty: highnovelscott: mediumquantization low-bit latent-factorization local-inferenceBanseok LeeDongkyu KimYoungcheon YouYoungmin KimSamsung Labs

What is this?

Samsung Labs researchers (Banseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim) have posted arXiv:2506.13771 "LittleBit: Ultra Low-Bit Quantization via Latent Factorization" claiming a novel method achieving 0.1 bits-per-weight (BPW) quantization that surpasses leading techniques operating at 0.7 BPW. The paper proposes latent factorization as the core mechanism for this ultra-low-bit compression. No independent web coverage or replication results were found in the search window (the query timed out with zero organic results), so the claims rest solely on the preprint at this stage.

Why it matters to Scott

Touches Scott's actual local-inference stack: a validated 0.1 BPW method would change which models fit on gamepc's RTX-3090/Ollama setup and how he quantizes for local serving — but the claims are unverified preprint-only with zero independent coverage, so it's a watch-for-replication item, not yet a change to what he builds. Nothing in his wikis states a position on ultra-low-bit latent factorization, so this is new territory rather than confirmation of an argued stance.

dev:concept.hardware-aware-local-inferencedev:project.gamepcdev:technology.ollamaradar:glm-lossless-weight-compressionradar:gsq-rco-qwen38-flashnext-quantsradar:adaptive-kv-cache-streamingradar:exans-lossless-kv-cache-compression

queries asked of Scott's wikis

quantization ultra-low-bit 0.1 BPW latent factorization local inference
model compression quality-size frontier local LLM deployment
open-weight model quantization strategy sovereignty
inference economics sub-1-bit quantization tradeoffs
latent factorization vs GPTQ/AWQ/QLoRA compression approaches

Measured heat

now 0 pts/hpeak 46 pts/hcomments 0/hpeers p14momentum: steady3 platformsage 12002h

points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

05-29 14:00	⭐ origin echo-reconstructed	Abstract (v1 submitted 30 May 2025): "we introduce LittleBit, a novel framework for extreme LLM compression. We target quantization rates as Banseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim (Samsung Labs / Samsung Research; Lee and Kim contributed equally) on paper (echo) · attributed from reddit.post.1x0sa6g	—
10-08 13:29	first on hacker news · published · +11927.5h	Sub-1-Bit LLM Compression via Latent Factorization brainless	—
10-08 14:23	first on r/LocalLLaMA · published · +11928.4h	[2506.13771] LittleBit: Ultra Low-Bit Quantization via Latent Factorization sn2006gy	—
10-08 13:29	amplified on hacker news 👑	hn.story.50005608 brainless	peak 77 · 22 comments · 64% of case engagement
10-08 14:23	amplified on r/LocalLLaMA	reddit.post.1x0sa6g sn2006gy	peak 78 · 21 comments · 36% of case engagement
10-08 15:37	our radar first saw it · +11929.6h	discovery anchor: reddit.post.1x0sa6g	—

Evidence (3) — ⭐ canonical anchor

source	object	author	score	comments
🟠 reddit	[2506.13771] LittleBit: Ultra Low-Bit Quantization via Latent Factorization LocalLLaMA Retrieved article excerpt Open article · Retrieved 2026-10-08T15:50:40.808282+00:00 # Computer Science > Machine Learning arXiv:2506.13771 (cs) [Submitted on 30 May 2025 ([v1](https://arxiv.org/abs/2506.13771v1)), last revised 5 Feb 2026 (this version, v5)] # Title:LittleBit: Ultra Low-Bit Quantization via Latent Factorization Authors:[Banseok Lee](https://arxiv.org/search/cs?searchtype=author&query=Lee,+B), [Dongkyu Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+D), [Youngcheon You](https://arxiv.org/search/cs?searchtype=author&query=You,+Y), [Youngmin Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+Y) View a PDF of the paper titled LittleBit: Ultra Low-Bit Quantization via Latent Factorization, by Banseok Lee and 3 other authors [View PDF](https://arxiv.org/pdf/2506.13771) [HTML (experimental)](https://arxiv.org/html/2506.13771v5) > Abstract:The deployment of large language models (LLMs) is frequently hindered by prohibitive memory and computational requirements. While quantization mitigates these bottlenecks, maintaining model fidelity in the sub-1-bit regime remains a persistent challenge. In this paper, we introduce LittleBit, a novel framework for extreme LLM compression. We target quantization rates as low as $0.1$ bits per weight (BPW), achieving a memory reduction of approximately $31\times$, which effectively compresses Llama2-13B to under $0.9$ GB. We represent weights via low-rank latent matrix factorization and subsequently binarize the resulting factors. To counteract the information loss inherent to such drastic precision reduction, we integrate a multi-scale compensation mechanism that learns importance parameters across row, column, and latent dimensions. Two primary contributions enable effective training: Dual Sign-Value-Independent Decomposition (Dual-SVID) for quantization-aware training (QAT) initialization, and Residual Compensation to minimize approximation errors. Extensive experiments confirm the superiority of LittleBit in the sub-1-bit domain; for instance, our method at $0.1$ BPW surpasses the performance of leading techniques operating at $0.7$ BPW on Llama2-7B. We establish a new size-performance trade-off -- unlocking a potential $11.6\times$ inference speedup relative to FP16 -- and render powerful LLMs practical for resource-constrained environments. Our code is available at [this https URL](https://github.com/SamsungLabs/LittleBit). \| \| \| --- \| \| Comments: \| \| Subjects: \| Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) \| \| Cite as: \| [arXiv:2506.13771](https://arxiv.org/abs/2506.13771) [cs.LG] \| \| \| (or [arXiv:2506.13771v5](https://arxiv.org/abs/2506.13771v5) [cs.LG] for this version) \| \| \| <https://doi.org/10.48550/arXiv.2506.13771> Focus to learn more arXiv-issued DOI via DataCite \| ## Submission history From: Dongkyu Kim [[view email](https://arxiv.org/show-email/2af7be41/2506.13771)] [[v1]](https://arxiv.org/abs/2506.13771v1) Fri, 30 May 2025 06:43:03 UTC (4,465 KB) [[v2]](https://arxiv.org/abs/2506.13771v2) Tue, 28 Oct 2025 10:57:14 UTC (4,388 KB) [[v3]](https://arxiv.org/abs/2506.13771v3) Thu, 4 Dec 2025 22:56:58 UTC (4,380 KB) [[v4]](https://arxiv.org/abs/2506.13771v4) Thu, 15 Jan 2026 10:46:32 UTC (4,564 KB) [v5] Thu, 5 Feb 2026 01:59:26 UTC (4,587 KB) Full-text links: ## Access Paper: View a PDF of the paper titled LittleBit: Ultra Low-Bit Quantization via Latent Factorization, by Banseok Lee and 3 other authors - [View PDF](https://arxiv.org/pdf/2506.13771) - [HTML (experimental)](https://arxiv.org/html/2506.13771v5) - [TeX Source](https://arxiv.org/src/2506.13771) [license icon](http://creativecommons.org/licenses/by-nc-nd/4.0/ "Rights to this article") ### Current browse context: cs.LG [< prev](https://arxiv.org/prevnext?id=2506.13771&function=prev&context=cs.LG "previous in cs.LG (accesskey p)") \| [next >](https://arxiv.org/prevnext?id=2506.13771&function=next&context=cs.LG "next in cs.LG (accesskey n)") [new](https://arxiv.org/list/cs.LG/new) \| [recent](https://arxiv.org/list/cs.LG/recent) \| [2025-06](https://arxiv.org/list/cs.LG/2025-06) Change to browse by: [cs](https://arxiv.org/abs/2506.13771?context=cs) [cs.AI](https://arxiv.org/abs/2506.13771?context=cs.AI) [cs.CL](https://arxiv.org/abs/2506.13771?context=cs.CL) ### References & Citations - [NASA ADS](https://ui.adsabs.harvard.edu/abs/arXiv:2506.13771) - [Google Scholar](https://scholar.google.com/scholar_lookup?arxiv_id=2506.13771) - [Semantic Scholar](https://api.semanticscholar.org/arXiv:2506.13771) export BibTeX citation Loading... ## BibTeX formatted citation × loading... Data provided by: ### Bookmark [BibSonomy](http://www.bibsonomy.org/BibtexHandler?requTask=upload&url=https://arxiv.org/abs/2506.13771&description=LittleBit: Ultra Low-Bit Quantization via Latent Factorization "Bookmark on BibSonomy") [Reddit](https://reddit.com/submit?url=https://arxiv.org/abs/2506.13771&title=LittleBit: Ultra Low-Bit Quantization via Latent Factorization "Bookmark on Reddit") Bibliographic Tools # Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ([What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer)) Connected Papers Toggle Connected Papers ([What is Connected Papers?](https://www.connectedpapers.com/about)) Litmaps Toggle Litmaps ([What is Litmaps?](https://www.litmaps.co/)) scite.ai Toggle scite Smart Citations ([What are Smart Citations?](https://www.scite.ai/)) Code, Data, Media # Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ([What is alphaXiv?](https://alphaxiv.org/)) Links to Code Toggle CatalyzeX Code Finder for Papers ([What is CatalyzeX?](https://www.catalyzex.com)) DagsHub Toggle DagsHub ([What is DagsHub?](https://dagshub.com/)) GotitPub Toggle Gotit.pub ([What is GotitPub?](http://gotit.pub/faq)) Huggingface Toggle Hugging Face ([What is Huggingface?](https://huggingface.co/huggingface)) ScienceCast Toggle ScienceCast ([What is ScienceCast?](https://sciencecast.org/welcome)) Demos # Demos Replicate Toggle Replicate ([What is Replicate?](https://replicate.com/docs/arxiv/about)) Spaces Toggle Hugging Face Spaces ([What is Spaces?](https://huggingface.co/docs/hub/spaces)) Spaces Toggle TXYZ.AI ([What is TXYZ.AI?](https://txyz.ai)) Related Papers # Recommenders and Search Tools Link to Influence Flower Influence Flower ([What are Influence Flowers?](https://influencemap.cmlab.dev/)) Core recommender toggle CORE Recommender ([What is CORE?](https://core.ac.uk/services/recommender)) IArxiv recommender toggle IArxiv Recommender ([What is IArxiv?](https://iarxiv.org/about)) - Author - Venue - Institution - Topic About arXivLabs # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? [Learn more about arXivLabs](https://info.arxiv.org/labs/index.html). [Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2506.13771) \| Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))	sn2006gy	78	21
🟧 echo.paper ⭐	Abstract (v1 submitted 30 May 2025): "we introduce LittleBit, a novel framework for extreme LLM compression. We target quantization rates as	Banseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim (Samsung Labs / Samsung Research; Lee and Kim contributed equally)	—	—
🟧 hn	Sub-1-Bit LLM Compression via Latent Factorization	brainless	77	22

source

object

author

score

comments

🟠 reddit

[2506.13771] LittleBit: Ultra Low-Bit Quantization via Latent Factorization

LocalLLaMA

Retrieved article excerpt

Open article · Retrieved 2026-10-08T15:50:40.808282+00:00

# Computer Science > Machine Learning

**arXiv:2506.13771** (cs)

[Submitted on 30 May 2025 ([v1](https://arxiv.org/abs/2506.13771v1)), last revised 5 Feb 2026 (this version, v5)]

# Title:LittleBit: Ultra Low-Bit Quantization via Latent Factorization

Authors:[Banseok Lee](https://arxiv.org/search/cs?searchtype=author&query=Lee,+B), [Dongkyu Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+D), [Youngcheon You](https://arxiv.org/search/cs?searchtype=author&query=You,+Y), [Youngmin Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+Y)

View a PDF of the paper titled LittleBit: Ultra Low-Bit Quantization via Latent Factorization, by Banseok Lee and 3 other authors

[View PDF](https://arxiv.org/pdf/2506.13771)
[HTML (experimental)](https://arxiv.org/html/2506.13771v5)
> Abstract:The deployment of large language models (LLMs) is frequently hindered by prohibitive memory and computational requirements. While quantization mitigates these bottlenecks, maintaining model fidelity in the sub-1-bit regime remains a persistent challenge. In this paper, we introduce LittleBit, a novel framework for extreme LLM compression. We target quantization rates as low as $0.1$ bits per weight (BPW), achieving a memory reduction of approximately $31\times$, which effectively compresses Llama2-13B to under $0.9$ GB. We represent weights via low-rank latent matrix factorization and subsequently binarize the resulting factors. To counteract the information loss inherent to such drastic precision reduction, we integrate a multi-scale compensation mechanism that learns importance parameters across row, column, and latent dimensions. Two primary contributions enable effective training: Dual Sign-Value-Independent Decomposition (Dual-SVID) for quantization-aware training (QAT) initialization, and Residual Compensation to minimize approximation errors. Extensive experiments confirm the superiority of LittleBit in the sub-1-bit domain; for instance, our method at $0.1$ BPW surpasses the performance of leading techniques operating at $0.7$ BPW on Llama2-7B. We establish a new size-performance trade-off -- unlocking a potential $11.6\times$ inference speedup relative to FP16 -- and render powerful LLMs practical for resource-constrained environments. Our code is available at [this https URL](https://github.com/SamsungLabs/LittleBit).

|  |
| --- |
| Comments: |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | [arXiv:2506.13771](https://arxiv.org/abs/2506.13771) [cs.LG] |
|  | (or  [arXiv:2506.13771v5](https://arxiv.org/abs/2506.13771v5) [cs.LG] for this version) |
|  | <https://doi.org/10.48550/arXiv.2506.13771> Focus to learn more  arXiv-issued DOI via DataCite |

## Submission history

From: Dongkyu Kim [[view email](https://arxiv.org/show-email/2af7be41/2506.13771)]   
 **[[v1]](https://arxiv.org/abs/2506.13771v1)**
Fri, 30 May 2025 06:43:03 UTC (4,465 KB)  
**[[v2]](https://arxiv.org/abs/2506.13771v2)**
Tue, 28 Oct 2025 10:57:14 UTC (4,388 KB)  
**[[v3]](https://arxiv.org/abs/2506.13771v3)**
Thu, 4 Dec 2025 22:56:58 UTC (4,380 KB)  
**[[v4]](https://arxiv.org/abs/2506.13771v4)**
Thu, 15 Jan 2026 10:46:32 UTC (4,564 KB)  
**[v5]**
Thu, 5 Feb 2026 01:59:26 UTC (4,587 KB)

Full-text links:

## Access Paper:

View a PDF of the paper titled LittleBit: Ultra Low-Bit Quantization via Latent Factorization, by Banseok Lee and 3 other authors

- [View PDF](https://arxiv.org/pdf/2506.13771)
- [HTML (experimental)](https://arxiv.org/html/2506.13771v5)
- [TeX Source](https://arxiv.org/src/2506.13771)

[license icon](http://creativecommons.org/licenses/by-nc-nd/4.0/ "Rights to this article")

### Current browse context:

cs.LG

[< prev](https://arxiv.org/prevnext?id=2506.13771&function=prev&context=cs.LG "previous in cs.LG (accesskey p)")
  |   
[next >](https://arxiv.org/prevnext?id=2506.13771&function=next&context=cs.LG "next in cs.LG (accesskey n)")

[new](https://arxiv.org/list/cs.LG/new)
 | 
[recent](https://arxiv.org/list/cs.LG/recent)
 | [2025-06](https://arxiv.org/list/cs.LG/2025-06)

Change to browse by:

[cs](https://arxiv.org/abs/2506.13771?context=cs)  
[cs.AI](https://arxiv.org/abs/2506.13771?context=cs.AI)  
[cs.CL](https://arxiv.org/abs/2506.13771?context=cs.CL)

### References & Citations

- [NASA ADS](https://ui.adsabs.harvard.edu/abs/arXiv:2506.13771)
- [Google Scholar](https://scholar.google.com/scholar_lookup?arxiv_id=2506.13771)
- [Semantic Scholar](https://api.semanticscholar.org/arXiv:2506.13771)

export BibTeX citation
Loading...

## BibTeX formatted citation

×

loading...

Data provided by:

### Bookmark

[BibSonomy](http://www.bibsonomy.org/BibtexHandler?requTask=upload&url=https://arxiv.org/abs/2506.13771&description=LittleBit: Ultra Low-Bit Quantization via Latent Factorization "Bookmark on BibSonomy")
[Reddit](https://reddit.com/submit?url=https://arxiv.org/abs/2506.13771&title=LittleBit: Ultra Low-Bit Quantization via Latent Factorization "Bookmark on Reddit")



Bibliographic Tools

# Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer *([What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))*

Connected Papers Toggle

Connected Papers *([What is Connected Papers?](https://www.connectedpapers.com/about))*

Litmaps Toggle

Litmaps *([What is Litmaps?](https://www.litmaps.co/))*

scite.ai Toggle

scite Smart Citations *([What are Smart Citations?](https://www.scite.ai/))*

Code, Data, Media

# Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv *([What is alphaXiv?](https://alphaxiv.org/))*

Links to Code Toggle

CatalyzeX Code Finder for Papers *([What is CatalyzeX?](https://www.catalyzex.com))*

DagsHub Toggle

DagsHub *([What is DagsHub?](https://dagshub.com/))*

GotitPub Toggle

Gotit.pub *([What is GotitPub?](http://gotit.pub/faq))*

Huggingface Toggle

Hugging Face *([What is Huggingface?](https://huggingface.co/huggingface))*

ScienceCast Toggle

ScienceCast *([What is ScienceCast?](https://sciencecast.org/welcome))*

Demos

# Demos

Replicate Toggle

Replicate *([What is Replicate?](https://replicate.com/docs/arxiv/about))*

Spaces Toggle

Hugging Face Spaces *([What is Spaces?](https://huggingface.co/docs/hub/spaces))*

Spaces Toggle

TXYZ.AI *([What is TXYZ.AI?](https://txyz.ai))*

Related Papers

# Recommenders and Search Tools

Link to Influence Flower

Influence Flower *([What are Influence Flowers?](https://influencemap.cmlab.dev/))*

Core recommender toggle

CORE Recommender *([What is CORE?](https://core.ac.uk/services/recommender))*

IArxiv recommender toggle

IArxiv Recommender
*([What is IArxiv?](https://iarxiv.org/about))*

- Author
- Venue
- Institution
- Topic


About arXivLabs

# arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2506.13771) |
Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))

sn2006gy

🟧 echo.paper ⭐

Abstract (v1 submitted 30 May 2025): "we introduce LittleBit, a novel framework for extreme LLM compression. We target quantization rates as

Banseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim (Samsung Labs / Samsung Research; Lee and Kim contributed equally)

—

🟧 hn

Sub-1-Bit LLM Compression via Latent Factorization

brainless

Interpretation history

2026-10-08T21:23:37Z
Multi-platform discussion emerged (Reddit + HN) with code available, but HN commentary highlights significant quality degradation (perplexity 80%→47% on Wikitext-2) and lack of FP4 comparisons; remains unverified preprint with no independent replication.
2026-10-08T17:49:20Z
evidence attached: hn.story.50005608 — HN post directly links the Samsung Labs LittleBit repo and sub-1-bit quantization claim that the open case tracks
2026-10-08T16:04:56Z
origin walked (opencode/cheap-glm, conf 0.92): anchor reddit.post.1x0sa6g -> echo.paper.4f1b61cb25 by Banseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim (Samsung Labs / Samsung Research; Lee and Kim contributed equally)
2026-10-08T16:03:37Z
grounded: novel/medium — Touches Scott's actual local-inference stack: a validated 0.1 BPW method would change which models fit on gamepc's RTX-3090/Ollama setup and how he quantizes fo
2026-10-08T15:51:55Z
case created — New arXiv paper from Samsung Labs proposing a novel ultra-low-bit quantization method with claimed SOTA results at 0.1 BPW; potential to shift local inference economics if adopted.

Decision trace

10-09 10:06attention_routeThe editor compared this story and chose to keep watching.
10-09 09:34sensor_dirtycomment_update
10-09 09:34sensor_dirtycomment_update
10-09 09:34sensor_dirtycomment_update
10-09 08:23repriceMulti-platform discussion emerged (Reddit + HN) with code available, but HN commentary highlights significant quality degradation (perplexity 80%→47% on Wikitext-2) and lack of FP4 comparisons; remain
10-09 04:55attention_routeNew seed-case relevant to Scott's local-inference stack; unverified but NeurIPS-accepted — flag in briefing for awareness.
10-09 04:49attention_candidateattach
10-09 04:49attachHN post directly links the Samsung Labs LittleBit repo and sub-1-bit quantization claim that the open case tracks
10-09 04:44propose_attachHN post directly links the Samsung Labs LittleBit repo and sub-1-bit quantization claim that the open case tracks
10-09 04:35sensor_dirtyvelocity_spike
10-09 03:06attention_routeNew seed-case relevant to Scott's local-inference stack (gamepc/RTX-3090/Ollama); unverified but NeurIPS-accepted — flag in briefing for awareness.
10-09 03:04attention_candidatecreate
10-09 03:04promote_anchororigin walk conf 0.92
10-09 03:03groundTouches Scott's actual local-inference stack: a validated 0.1 BPW method would change which models fit on gamepc's RTX-3090/Ollama setup and how he quantizes for local serving — but the clai
10-09 02:51createNew arXiv paper from Samsung Labs proposing a novel ultra-low-bit quantization method with claimed SOTA results at 0.1 BPW; potential to shift local inference economics if adopted.