# The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

**Authors:** Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu Wei  
**arXiv:** [2402.17764](https://arxiv.org/abs/2402.17764)  
**Date:** 2024-02-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.17764v1/

## Key points

- Recent research in Large Language Models (LLMs) has led to the development of BitNet, paving the way for a new era of 1-bit LLMs.
- A novel variant called BitNet b1.58 represents every parameter in ternary form {-1, 0, 1}, demonstrating comparable performance to full-precision Transformer LLMs like FP16 or BF16 models.
- BitNet b1.58 excels in latency, memory efficiency, throughput, and energy consumption while offering significant cost-effectiveness advantages.
- This advancement sets forth a new scaling law and methodology for training high-performing and economically viable LLMs with implications for specialized hardware design.
- The research signifies a shift towards more efficient language processing systems with far-reaching implications for artificial intelligence and machine learning technologies.

## Summary

The recent research in the field of Large Language Models (LLMs) has led to the development of a groundbreaking innovation known as BitNet, which is paving the way for a new era of 1-bit LLMs. In this study, a novel variant of 1-bit LLM called BitNet b1.58 has been introduced, representing every parameter or weight of the model in ternary form {-1, 0, 1}. Remarkably, this 1.58-bit LLM demonstrates comparable performance to full-precision Transformer LLMs like FP16 or BF16 models in terms of perplexity and end-task outcomes while offering significant advantages in cost-effectiveness. The BitNet b1.58 not only matches its counterparts in performance but also excels in latency, memory efficiency, throughput, and energy consumption. This advancement sets forth a new scaling law and methodology for training future generations of high-performing and economically viable LLMs. Furthermore, it opens up possibilities for designing specialized hardware optimized specifically for 1-bit LLMs. can greatly enhance computational efficiency and revolutionize various applications across industries. The implications of this research extend beyond just improving existing models; it signifies a shift towards more efficient and effective language processing systems. The authors behind this pioneering work include Shuming Ma, Hongyu Wang, Lingxiao Ma,
Lei Wang,, Shaohan Huang,, Ruiping Wang,
Jilong Xue,. Their collaborative efforts have resulted in a significant advancement in the field of language modeling with far-reaching implications for future developments in artificial intelligence and machine learning technologies.