Microsoft researchers build 1-bit AI LLM with 2B parameters — model small enough to run on some CPUs

Microsoft researchers just created BitNet b1.58 2B4T, an open-source 1-bit large language model (LLM) with two billion parameters trained on four trillion tokens. But what makes this AI model unique is that it’s lightweight enough to work efficiently on a CPU, with TechCrunch saying an Apple M2 chip can run it. The model is also readily available on Hugging Face, allowing anyone to experiment with it.

Bitnets use 1-bit weights with only three possible values: -1, 0, and +1 — technically it’s a “1.58-bit model” due to the support for three values. This saves a lot of memory compared to mainstream AI models with 32-bit or 16-bit floating-point formats, allowing them to operate much more efficiently and require less memory and computational power. Bitnet’s simplicity has one drawback, though — it’s less accurate compared to larger AI models. However, BitNet b1.58 2B4T makes up for this with its massive training data, which is estimated to be more than 33 million books.