BitNet-b1.58-2B-4T released by Microsoft Research in April 2025 is a major breakthrough in the field of artificial intelligence. As the first open source native 1-bit large model, it breaks through the limitations of traditional quantization technology and proves that low-precision models can significantly reduce computing resource consumption while maintaining performance, paving the way for local AI deployment on end devices.BitNet b1.58 2B4T Technical Report".
This tutorial uses BitNet-b1.58-2B-4T as a demonstration, the image uses PyTorch 2.6-2204, and the computing resource uses RTX 4090.
2. Core Features
Efficient architecture: Using ternary quantized weights (-1, 0, +1), each weight only requires 1.58 bits of storage. Combined with 8-bit activation values (W1.58A8 configuration), the non-embedded memory usage is only 0.4 GB, which is much lower than similar models (such as Gemma-3 1B's 1.4 GB).
Training innovation: training from scratch (not post-quantization), introducing BitLinear layers, squared ReLU activation functions, and RoPE position encoding to ensure the stability of low-precision training.
Energy consumption advantage: CPU inference latency is as low as 29 milliseconds, and energy consumption is only 0.028 joules/token, supporting efficient operation on CPUs such as Apple M2.
3. Operation steps
1. After starting the container, click the API address to enter the Web interface
If "Bad Gateway" is displayed, it means the model is initializing. Since the model is large, please wait about 1-2 minutes and refresh the page.
2. Functional Demonstration
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