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arXiv · 2402.17764

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

Shuming Ma·Hongyu Wang·Lingxiao Ma·Lei Wang·Wenhui Wang·Shaohan Huang·Li Dong·Ruiping Wang·Jilong Xue·Furu Wei
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Abstract

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.

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Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu Wei. 2024-02-27. The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. https://arxiv.org/abs/2402.17764

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