Microsoft’s model BitNet b1.58 2B4T is available on Hugging Face but doesn’t run on GPU and requires a proprietary framework.

Memory requirements are the most obvious advantage of reducing the complexity of a model's internal weights. The BitNet b1.58 ...

matrix multiplication (LLM.int8()), and 8 & 4-bit quantization functions. There are ongoing efforts to support further hardware backends, i.e. Intel CPU + GPU, AMD GPU, Apple Silicon, hopefully NPU.

Microsoft’s new BitNet b1.58 model significantly reduces memory and energy requirements while matching the capabilities of ...

native 1-bit LLM trained at scale», with 2 billion parameters and a training dataset of 4 trillion tokens. Unlike previous post-training quantization attempts, which often degrade performance ...

Running GenAI models is easy. Scaling them to thousands of users, not so much Hands On You can spin up a chatbot with ...