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1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs

Nov 17, 2024 - arxiv.org
The article discusses recent advancements in 1-bit Large Language Models (LLMs) such as BitNet and BitNet b1.58, which have improved the speed and energy efficiency of LLMs and enabled their deployment across various devices. The authors introduce a tailored software stack designed to maximize the potential of 1-bit LLMs. They have developed a set of kernels for fast and lossless inference of ternary BitNet b1.58 LLMs on CPUs.

The software has been extensively tested and has shown significant speed improvements, with speedups ranging from 2.37x to 6.17x on x86 CPUs and from 1.37x to 5.07x on ARM CPUs, across various model sizes. The code for this software is publicly available. The article was submitted by Shaoguang Mao and has been updated twice since its initial submission.

Key takeaways:

  • Recent advances in 1-bit Large Language Models (LLMs), such as BitNet and BitNet b1.58, have improved the efficiency of LLMs in terms of speed and energy consumption.
  • These developments have enabled local LLM deployment across a wide range of devices.
  • A new software stack has been introduced, specifically designed to maximize the potential of 1-bit LLMs.
  • Extensive experiments show that this software achieves significant speedups, ranging from 2.37x to 6.17x on x86 CPUs and from 1.37x to 5.07x on ARM CPUs, across various model sizes.
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