LittleBit, Samsung's method to compress 13B LLMs below 1 GB
According to the report, Samsung open-sourced LittleBit, a method that uses latent factorization to shrink weights of 13 billion parameter models to under 1 GB, with sub-1-bit levels and XOR in place of multiplication.
Radar records that Samsung open-sourced LittleBit, a language model compression method. According to the source's author, the technique uses latent factorization to compress the weights of 13 billion parameter LLMs to under 1 GB, operating at sub-1-bit precision levels and replacing multiplication with XOR operations.
The source indicates that the topic interests engineers who evaluate sub-1-bit quantization for local model deployment. It also makes clear that the speed gains are the author's claims, with no independent measurement described in the text. The information should therefore be treated as a report until the code and results are examined directly.
To consult the original source, look for the LittleBit open-source announcement in the channel the report's author points to, and check the published repository, its license, and the reported benchmarks, including model, hardware and metrics.