---
format: "aidr-story-markdown/v1"
id: "317f19d8e74db663e19a6d8be49dc047c6eec64f1c04d99295c0d78341a3920b"
canonical_url: "https://aidr.today/317f19d8?lang=en"
title: "Samsung Shrinks 13B LLM to Under 1 GB for Local AI"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-08T14:10:11.000Z"
category: "Open Source"
topics: ["llm","open-source"]
source_urls: ["https://huggingnews.com/ai/samsung-shrinks-13b-llm-to-under-1-gb-for-local-ai-35b661b8","https://marketbrief.now/ai/samsung-shrinks-13b-llm-to-under-1-gb-for-local-ai-35b661b8"]
summary: "Samsung developed LittleBit, an open-source compression method that reduces a 13B parameter large language model to under 1 GB of memory. The technique replaces floating-point multiplication with bitwise XOR operations and uses latent factorization to reduce weights to as low as 0.1 bits per weight. It provides an 11.6x inference speedup compared to standard FP16 models and allows state-of-the-art AI to run locally on resource-constrained devices."
---

# Samsung Shrinks 13B LLM to Under 1 GB for Local AI

> [Open the canonical story](<https://aidr.today/317f19d8?lang=en>)

**Published:** 2026-10-08T14:10:11.000Z
**Category:** Open Source
**Topics:** llm, open\-source

## Summary

Samsung developed LittleBit, an open\-source compression method that reduces a 13B parameter large language model to under 1 GB of memory\. The technique replaces floating\-point multiplication with bitwise XOR operations and uses latent factorization to reduce weights to as low as 0\.1 bits per weight\. It provides an 11\.6x inference speedup compared to standard FP16 models and allows state\-of\-the\-art AI to run locally on resource\-constrained devices\.

## Sources

- [Story source](<https://huggingnews.com/ai/samsung-shrinks-13b-llm-to-under-1-gb-for-local-ai-35b661b8>)
- [Story source](<https://marketbrief.now/ai/samsung-shrinks-13b-llm-to-under-1-gb-for-local-ai-35b661b8>)