What is a 1-bit LLM?
Understanding 1-bit quantization for large language models and efficient AI inference
Definition: 1-bit Large Language Model
A 1-bit LLM (Large Language Model) is a language model whose weights are stored and computed using only two values—typically -1 and +1 (or 0 and 1)—instead of 16-bit or 32-bit floating-point numbers. This extreme quantization reduces memory use by up to 16x and allows faster inference on the same hardware.
Why 1-bit Quantization?
Traditional LLMs use FP16 or FP32 weights, which need significant GPU memory and bandwidth. 1-bit quantization:
- Cuts memory use — Same model fits in far less RAM or VRAM.
- Speeds up inference — Fewer bits mean faster matrix operations with optimized kernels.
- Lowers cost — Enables running models on cheaper or smaller devices.
- Enables edge deployment — Run LLMs on laptops, workstations, or embedded systems.
How BitNet Implements 1-bit LLMs
BitNet is Microsoft's inference framework for 1-bit LLMs. It uses custom CUDA kernels and the GGUF format to run models like BitNet-b1.58 and Falcon3-1.58bit efficiently. Learn more in our features and getting started guides.