BitNet: Scaling 1-bit Transformers for Large Language Models
This work addresses efficiency problems for deploying large language models, offering a scalable solution with potential environmental benefits, though it is incremental as it builds on existing quantization methods.
The authors tackled the high energy consumption and deployment challenges of large language models by introducing BitNet, a 1-bit Transformer architecture that reduces memory footprint and energy consumption while achieving competitive performance compared to 8-bit quantization and FP16 baselines.
The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. Furthermore, BitNet exhibits a scaling law akin to full-precision Transformers, suggesting its potential for effective scaling to even larger language models while maintaining efficiency and performance benefits.