Project Title

BitNet — Official Inference Framework for 1-bit Large Language Models

Overview

BitNet is an official inference framework designed for 1-bit Large Language Models (LLMs), offering optimized kernels for fast and lossless inference on both CPU and GPU. This project stands out for its ability to significantly reduce energy consumption while enhancing performance, making it ideal for running LLMs on local devices.

Key Features

Optimized kernels for 1.58-bit models on CPU and GPU
Speedups of 1.37x to 5.07x on ARM CPUs and 2.37x to 6.17x on x86 CPUs
Energy consumption reduction by 55.4% to 70.0% on ARM and 71.9% to 82.2% on x86
Capable of running a 100B BitNet b1.58 model on a single CPU

Use Cases

Researchers and developers needing efficient inference for large language models
Enterprises looking to deploy LLMs on local devices with reduced energy consumption
Academics and institutions conducting research on low-bit LLMs

Advantages

Enhanced performance with significant speedups on various CPU architectures
Substantial energy savings, improving overall efficiency
Supports large-scale models, enabling local device deployment

Limitations / Considerations

NPU support is upcoming and not yet available
The framework is specialized for 1-bit LLMs and may not be suitable for other types of models

llama.cpp: A framework that BitNet is based on, focusing on efficient inference for large language models.
T-MAC: A project that BitNet's kernels are built upon, focusing on Lookup Table methodologies for low-bit LLMs.

Basic Information

GitHub: https://github.com/microsoft/BitNet
Stars: 21,665
License: MIT
Last Commit: 2025-09-07

📊 Project Information

Project Name: BitNet
GitHub URL: https://github.com/microsoft/BitNet
Programming Language: Python
⭐ Stars: 21,665
🍴 Forks: 1,641
📅 Created: 2024-08-05
🔄 Last Updated: 2025-09-07

🏷️ Project Topics

Topics: [, ]

🎮 Online Demos

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BitNet

Project Description