bitnet.cpp

📰 News

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 — detailed guide for converting and running BitNet embedding models with optimized I2_S kernels.

01/15/2026: 📣 Released BitNet CPU Inference Optimization — 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 — extending 1-bit inference beyond CPUs.

04/14/2025: 📣 Released BitNet Official 2B Parameter Model 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 — system-level paper on bitnet.cpp's architecture and design.

11/08/2024: 📑 BitNet a4.8: 4-bit Activations for 1-bit LLMs — 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 — 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

02/27/2024: 📑 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits — the foundational paper introducing BitNet b1.58.

10/17/2023: 📑 BitNet: Scaling 1-bit Transformers for Large Language Models — 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, or build and run it on your own CPU or GPU.

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 for more details.

Model Releases

1. 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 | 🔗 Online Demo | 📄 Technical Report

2. 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 | 📄 I2_S Guide

3. 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 | 📄 I2_S Guide

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 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. 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

    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

git clone --recursive https://github.com/microsoft/BitNet.git
cd BitNet

2. Install the dependencies

# (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

# 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

# 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:

demo.mp4

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:

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:

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

# 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 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. 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 in the discussion 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.