Recent research is paving the way for a new era of 1-bit Large Language Models (LLMs). Introducing BitNet b1.58 - a ternary model with significant cost-effectiveness. Defines a new scaling law and recipe for high-performance, cost-effective LLMs. Enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs. Post-training quantization reduces precision for inference efficiency. BitNet and other 1-bit model architectures show promise for reducing LLM costs while maintaining performance. Matrix multiplication is a major cost in LLM computation, making lower bit models a cost-effective solution.
