# bitnet.cpp [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) ![version](https://img.shields.io/badge/version-1.0-blue) [![Hugging Face](https://img.shields.io/badge/HuggingFace-Collection-orange?logo=huggingface)](https://huggingface.co/collections/microsoft/bitnet) [![Technical Report](https://img.shields.io/badge/Technical-Report-red?logo=arxiv)](https://arxiv.org/abs/2502.11880) [![Demo](https://img.shields.io/badge/Online-Demo-green?logo=microsoft)](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/) [![GPU Kernel](https://img.shields.io/badge/GPU-Kernel-6F42C1?logo=github)](https://github.com/microsoft/BitNet/blob/main/gpu/README.md)

📰 News

07/23/2026: 📣 We released VibeASR.cpp — a real-time multilingual ASR inference engine on CPU using BitNet I2_S quantization, achieving RTF < 1 with very few threads on x86 (AVX2) and ARM (NEON) platforms. [Code] [Models] [Report] ![NEW](https://img.shields.io/badge/NEW-red) 07/20/2026: 📣 We released BitNet-embedding-0.6B and BitNet-embedding-270M on Hugging Face — the first 1-bit embedding models that deliver competitive embedding quality with significantly faster inference on CPUs. - **1.42x to 2.28x speedup** over F16 on BitNet-embedding-0.6B prefill (8 threads) - **1.32x to 1.74x speedup** over F16 on BitNet-embedding-270M prefill (8 threads) - Supports I2_S conversion with optimized kernels on x86 CPUs - Lossless inference with 2 bits per weight 07/16/2026: 📣 Released [BitNet Embeddings 0.6B/270M: I2_S Conversion and Inference Optimization](docs/bitnet-embeddings-i2s-guide.md) — detailed guide for converting and running BitNet embedding models with optimized I2_S kernels. 01/15/2026: 📣 Released [BitNet CPU Inference Optimization](https://github.com/microsoft/BitNet/blob/main/src/README.md) — parallel kernel implementations with configurable tiling and embedding quantization support, achieving **1.15x to 2.1x** additional speedup over the original implementation. 05/20/2025: 📣 Released [BitNet Official GPU inference kernel](https://github.com/microsoft/BitNet/blob/main/gpu/README.md) — extending 1-bit inference beyond CPUs. 04/14/2025: 📣 Released [BitNet Official 2B Parameter Model](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) on Hugging Face — the first official BitNet b1.58 model trained with 4T tokens. 02/18/2025: 📑 [Bitnet.cpp: Efficient Edge Inference for Ternary LLMs](https://arxiv.org/abs/2502.11880) — system-level paper on bitnet.cpp's architecture and design. 11/08/2024: 📑 [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://arxiv.org/abs/2411.04965) — enabling 4-bit activations for further efficiency gains. 10/21/2024: 📑 [1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs](https://arxiv.org/abs/2410.16144) — the technical report behind bitnet.cpp. 10/17/2024: 📣 bitnet.cpp 1.0 released. 03/21/2024: 📑 [The-Era-of-1-bit-LLMs: Training Tips, Code, FAQ](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf) 02/27/2024: 📑 [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764) — the foundational paper introducing BitNet b1.58. 10/17/2023: 📑 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453) — the original BitNet paper.
## Overview bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels that support **fast** and **lossless** inference of 1.58-bit models on **CPU** and **GPU** (NPU support coming next). Try it out via this [online demo](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/), or build and run it on your own [CPU](https://github.com/microsoft/BitNet?tab=readme-ov-file#build-from-source) or [GPU](https://github.com/microsoft/BitNet/blob/main/gpu/README.md). bitnet.cpp achieves speedups of **1.37x** to **5.07x** on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by **55.4%** to **70.0%**, further boosting overall efficiency. On x86 CPUs, speedups range from **2.37x** to **6.17x** with energy reductions between **71.9%** to **82.2%**. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the [technical report](https://arxiv.org/abs/2410.16144) for more details. performance_comparison ## Model Releases ### 1. [BitNet-b1.58-2B-4T](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) - 1-bit Large Language Model **BitNet-b1.58-2B-4T** is the first official BitNet b1.58 model with **2.4B parameters**, trained on **4 trillion tokens**. It is a ternary (1.58-bit) language model that delivers competitive performance with full-precision models of similar size while enabling significantly faster and more energy-efficient inference. - **Fast CPU Inference**: Achieves up to **6.17x speedup** on x86 CPUs and **5.07x** on ARM CPUs compared to full-precision models. - **Energy Efficient**: Reduces energy consumption by up to **82.2%** on x86 and **70.0%** on ARM. - **GPU Support**: Official GPU inference kernel available for accelerated deployment. - **Chat-Ready**: Supports conversational mode for interactive use. [🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) | [🔗 Online Demo](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/) | [📄 Technical Report](https://arxiv.org/abs/2410.16144) BitNet b1.58 2B Benchmark ### 2. [BitNet-embedding-0.6B](https://huggingface.co/microsoft/BitNet-embedding-0.6B) - 1-bit Embedding Model **BitNet-embedding-0.6B** is a **0.6B-parameter** 1-bit embedding model that achieves competitive embedding quality with significantly faster CPU inference. It is the first model to demonstrate that ternary weights can deliver strong performance on embedding tasks. - **1.42x to 2.28x speedup** over F16 on prefill (8 threads, x86) - **Lossless Quality**: Competitive embedding quality with 2 bits per weight - **I2_S Kernel**: Supports optimized I2_S conversion on x86 CPUs [🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-0.6B) | [📄 I2_S Guide](docs/bitnet-embeddings-i2s-guide.md) BitNet Embedding 0.6B Prefill Performance ### 3. [BitNet-embedding-270M](https://huggingface.co/microsoft/BitNet-embedding-270M) - Lightweight 1-bit Embedding Model **BitNet-embedding-270M** is a compact **270M-parameter** 1-bit embedding model designed for resource-constrained environments, offering fast inference with minimal memory footprint. - **1.32x to 1.74x speedup** over F16 on prefill (8 threads, x86) - **Lossless Quality**: Competitive embedding quality with 2 bits per weight - **Lightweight**: Only 270M parameters for edge deployment scenarios [🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-270M) | [📄 I2_S Guide](docs/bitnet-embeddings-i2s-guide.md) BitNet Embedding 270M Prefill Performance ## Supported Models
Model Parameters CPU Kernel
I2_S TL1 TL2
Official Models
BitNet-b1.58-2B-4T 2.4B x86 ✅ ❌ ✅
ARM ✅ ✅ ❌
BitNet-embedding-0.6B 0.6B x86 ✅ ❌ ❌
ARM ❌ ❌ ❌
BitNet-embedding-270M 270M x86 ✅ ❌ ❌
ARM ❌ ❌ ❌
Community Models
bitnet_b1_58-large 0.7B x86 ✅ ❌ ✅
ARM ✅ ✅ ❌
bitnet_b1_58-3B 3.3B x86 ❌ ❌ ✅
ARM ❌ ✅ ❌
Llama3-8B-1.58-100B-tokens 8.0B x86 ✅ ❌ ✅
ARM ✅ ✅ ❌
Falcon3 Family 1B-10B x86 ✅ ❌ ✅
ARM ✅ ✅ ❌
Falcon-E Family 1B-3B x86 ✅ ❌ ✅
ARM ✅ ✅ ❌
❗️**We use existing 1-bit LLMs available on [Hugging Face](https://huggingface.co/) to demonstrate the inference capabilities of bitnet.cpp. We hope the release of bitnet.cpp will inspire the development of 1-bit LLMs in large-scale settings in terms of model size and training tokens.** ## Installation ### Requirements - python>=3.10 - cmake>=3.22 - clang>=18 - For Windows users, install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/). In the installer, toggle on at least the following options(this also automatically installs the required additional tools like CMake): - Desktop-development with C++ - C++-CMake Tools for Windows - Git for Windows - C++-Clang Compiler for Windows - MS-Build Support for LLVM-Toolset (clang) - For Debian/Ubuntu users, you can download with [Automatic installation script](https://apt.llvm.org/) `bash -c "$(wget -O - https://apt.llvm.org/llvm.sh)"` - conda (highly recommend) ### Build from source > [!IMPORTANT] > If you are using Windows, please remember to always use a Developer Command Prompt / PowerShell for VS2022 for the following commands. Please refer to the FAQs below if you see any issues. 1. Clone the repo ```bash git clone --recursive https://github.com/microsoft/BitNet.git cd BitNet ``` 2. Install the dependencies ```bash # (Recommended) Create a new conda environment conda create -n bitnet-cpp python=3.10 conda activate bitnet-cpp pip install -r requirements.txt ``` 3. Build the project ```bash # Manually download the model and run with local path huggingface-cli download microsoft/BitNet-b1.58-2B-4T-gguf --local-dir models/BitNet-b1.58-2B-4T python setup_env.py -md models/BitNet-b1.58-2B-4T -q i2_s ``` 
usage: setup_env.py [-h] [--hf-repo {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}] [--model-dir MODEL_DIR] [--log-dir LOG_DIR] [--quant-type {i2_s,tl1}] [--quant-embd]
                    [--use-pretuned]

Setup the environment for running inference

optional arguments:
  -h, --help            show this help message and exit
  --hf-repo {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}, -hr {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}
                        Model used for inference
  --model-dir MODEL_DIR, -md MODEL_DIR
                        Directory to save/load the model
  --log-dir LOG_DIR, -ld LOG_DIR
                        Directory to save the logging info
  --quant-type {i2_s,tl1}, -q {i2_s,tl1}
                        Quantization type
  --quant-embd          Quantize the embeddings to f16
  --use-pretuned, -p    Use the pretuned kernel parameters
## Usage ### Basic usage ```bash # Run inference with the quantized model python run_inference.py -m models/BitNet-b1.58-2B-4T/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv ``` 
usage: run_inference.py [-h] [-m MODEL] [-n N_PREDICT] -p PROMPT [-t THREADS] [-c CTX_SIZE] [-temp TEMPERATURE] [-cnv]

Run inference

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Path to model file
  -n N_PREDICT, --n-predict N_PREDICT
                        Number of tokens to predict when generating text
  -p PROMPT, --prompt PROMPT
                        Prompt to generate text from
  -t THREADS, --threads THREADS
                        Number of threads to use
  -c CTX_SIZE, --ctx-size CTX_SIZE
                        Size of the prompt context
  -temp TEMPERATURE, --temperature TEMPERATURE
                        Temperature, a hyperparameter that controls the randomness of the generated text
  -cnv, --conversation  Whether to enable chat mode or not (for instruct models.)
                        (When this option is turned on, the prompt specified by -p will be used as the system prompt.)
### Demo A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2: https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1 ### Benchmark We provide scripts to run the inference benchmark providing a model. ``` usage: e2e_benchmark.py -m MODEL [-n N_TOKEN] [-p N_PROMPT] [-t THREADS] Setup the environment for running the inference required arguments: -m MODEL, --model MODEL Path to the model file. optional arguments: -h, --help Show this help message and exit. -n N_TOKEN, --n-token N_TOKEN Number of generated tokens. -p N_PROMPT, --n-prompt N_PROMPT Prompt to generate text from. -t THREADS, --threads THREADS Number of threads to use. ``` Here's a brief explanation of each argument: - `-m`, `--model`: The path to the model file. This is a required argument that must be provided when running the script. - `-n`, `--n-token`: The number of tokens to generate during the inference. It is an optional argument with a default value of 128. - `-p`, `--n-prompt`: The number of prompt tokens to use for generating text. This is an optional argument with a default value of 512. - `-t`, `--threads`: The number of threads to use for running the inference. It is an optional argument with a default value of 2. - `-h`, `--help`: Show the help message and exit. Use this argument to display usage information. For example: ```sh python utils/e2e_benchmark.py -m /path/to/model -n 200 -p 256 -t 4 ``` This command would run the inference benchmark using the model located at `/path/to/model`, generating 200 tokens from a 256 token prompt, utilizing 4 threads. For the model layout that do not supported by any public model, we provide scripts to generate a dummy model with the given model layout, and run the benchmark on your machine: ```bash python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile models/dummy-bitnet-125m.tl1.gguf --outtype tl1 --model-size 125M # Run benchmark with the generated model, use -m to specify the model path, -p to specify the prompt processed, -n to specify the number of token to generate python utils/e2e_benchmark.py -m models/dummy-bitnet-125m.tl1.gguf -p 512 -n 128 ``` ### Convert from `.safetensors` Checkpoints ```sh # Prepare the .safetensors model file huggingface-cli download microsoft/bitnet-b1.58-2B-4T-bf16 --local-dir ./models/bitnet-b1.58-2B-4T-bf16 # Convert to gguf model python ./utils/convert-helper-bitnet.py ./models/bitnet-b1.58-2B-4T-bf16 ``` ## Acknowledgements This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp) framework. We would like to thank all the authors for their contributions to the open-source community. Also, bitnet.cpp's kernels are built on top of the Lookup Table methodologies pioneered in [T-MAC](https://github.com/microsoft/T-MAC/). For inference of general low-bit LLMs beyond ternary models, we recommend using T-MAC. ### FAQ (Frequently Asked Questions)📌 #### Q1: The build dies with errors building llama.cpp due to issues with std::chrono in log.cpp? **A:** This is an issue introduced in recent version of llama.cpp. Please refer to this [commit](https://github.com/tinglou/llama.cpp/commit/4e3db1e3d78cc1bcd22bcb3af54bd2a4628dd323) in the [discussion](https://github.com/abetlen/llama-cpp-python/issues/1942) to fix this issue. #### Q2: How to build with clang in conda environment on windows? **A:** Before building the project, verify your clang installation and access to Visual Studio tools by running: ``` clang -v ``` This command checks that you are using the correct version of clang and that the Visual Studio tools are available. If you see an error message such as: ``` 'clang' is not recognized as an internal or external command, operable program or batch file. ``` It indicates that your command line window is not properly initialized for Visual Studio tools. • If you are using Command Prompt, run: ``` "C:\Program Files\Microsoft Visual Studio\2022\Professional\Common7\Tools\VsDevCmd.bat" -startdir=none -arch=x64 -host_arch=x64 ``` • If you are using Windows PowerShell, run the following commands: ``` Import-Module "C:\Program Files\Microsoft Visual Studio\2022\Professional\Common7\Tools\Microsoft.VisualStudio.DevShell.dll" Enter-VsDevShell 3f0e31ad -SkipAutomaticLocation -DevCmdArguments "-arch=x64 -host_arch=x64" ``` These steps will initialize your environment and allow you to use the correct Visual Studio tools.