+
# bitnet.cpp
+
[](https://opensource.org/licenses/MIT)
+[](https://huggingface.co/collections/microsoft/bitnet)
+[](https://arxiv.org/abs/2502.11880)
+[](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/)
+[](https://github.com/microsoft/BitNet/blob/main/gpu/README.md)
-[
](https://huggingface.co/microsoft/BitNet-embedding-0.6B)
+
-- **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)
+
📰 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](docs/bitnet-embeddings-i2s-guide.md) — detailed guide for converting and running BitNet embedding models with optimized I2_S kernels.
-For detailed usage and technical information, see the [BitNet Embeddings I2_S Guide](docs/bitnet-embeddings-i2s-guide.md).
+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.
-## About bitnet.cpp
+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.
-Try it out via this [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).
+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.
-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 will coming next).
+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.
-The first release of bitnet.cpp is to support inference on CPUs. 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.
+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.
-**Latest optimization** introduces parallel kernel implementations with configurable tiling and embedding quantization support, achieving **1.15x to 2.1x** additional speedup over the original implementation across different hardware platforms and workloads. For detailed technical information, see the [optimization guide](src/README.md).
+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.
+
+
-## Demo
+## Model Releases
-A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2:
+### 1. [BitNet-b1.58-2B-4T](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) - 1-bit Large Language Model
-https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
+**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.
-## What's New:
-- 07/20/2026 [BitNet-embedding-0.6B and BitNet-embedding-270M on Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-0.6B) 
-- 07/16/2026 [BitNet Embeddings 0.6B/270M: I2_S Conversion and Inference Optimization](docs/bitnet-embeddings-i2s-guide.md)
-- 01/15/2026 [BitNet CPU Inference Optimization](https://github.com/microsoft/BitNet/blob/main/src/README.md)
-- 05/20/2025 [BitNet Official GPU inference kernel](https://github.com/microsoft/BitNet/blob/main/gpu/README.md)
-- 04/14/2025 [BitNet Official 2B Parameter Model on Hugging Face](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T)
-- 02/18/2025 [Bitnet.cpp: Efficient Edge Inference for Ternary LLMs](https://arxiv.org/abs/2502.11880)
-- 11/08/2024 [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://arxiv.org/abs/2411.04965)
-- 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)
-- 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)
-- 10/17/2023 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453)
+- **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.
-## Acknowledgements
+[🤗 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)
+
+
+
+### 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)
+
+
+
+### 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)
+
+
+
+
+## Supported Models
-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.
-## Official Models