BitNet vs Other Methods

How 1-bit LLM inference compares to other quantization and runtimes

1-bit (BitNet) vs 4-bit / 8-bit Quantization

BitNet uses 1-bit weights (-1, +1), while methods like GPTQ or AWQ often use 4-bit or 8-bit. Result:

  • Memory: BitNet needs less memory per parameter (up to ~16x vs FP16). 4-bit needs about 4x less than FP16.
  • Speed: Fewer bits can mean faster matrix ops with dedicated kernels; BitNet uses custom CUDA for 1-bit.
  • Quality: 1-bit is more aggressive; BitNet models are trained or fine-tuned for this format. Quality depends on the model and task.

BitNet and llama.cpp

BitNet's C++ inference builds on ideas from the llama.cpp ecosystem and uses GGUF. It adds 1-bit-specific kernels and supports BitNet-b1.58 and other 1.58-bit variants. So BitNet is a specialized stack for 1-bit LLMs, not a general replacement for llama.cpp.

When to Choose BitNet

  • You want the smallest memory footprint for a given model size.
  • You are using or willing to use 1-bit / 1.58-bit models (e.g. from Microsoft or Hugging Face).
  • You care about efficient inference on consumer or edge hardware.

For broader model support (non-1-bit), tools like llama.cpp or vLLM may be better. See supported models and use cases.

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