Microsoft Bitnet

BitNet.cpp: inference framework cho 1.58-bit quantized LLMs — lossless, fast, energy-efficient. Chạy model 100B trên CPU thông thường.

Install

git clone https://github.com/microsoft/BitNet
cd BitNet
pip install -r requirements.txt
# Build: cmake + make

Usage

# Inference
python run_inference.py \
  -m models/BitNet-b1.58-2B-4T \
  -p "What is AI?" \
  -cnv

# Benchmark
python benchmark.py -m models/BitNet-b1.58-2B-4T --threads 4

# Convert SafeTensors → BitNet format
python convert_safetensors.py --input model.safetensors

Performance

ARM CPUs:  1.37x – 5.07x speedup | -55% – 70% energy
x86 CPUs:  2.37x – 6.17x speedup | -72% – 82% energy

100B model trên single CPU: 5-7 tokens/second

Supported Models

Microsoft:
  BitNet-b1.58-2B-4T (2.4B params) — official flagship

Community:
  BitNet 0.7B – 3.3B
  Llama3-8B-1.58
  Falcon3 1B – 10B
  Falcon-E 1B – 3B

Kernels: I2_S, TL1, TL2 (platform-specific)

Khi nào dùng

• Muốn chạy LLM local trên CPU không có GPU
• Edge deployment: embedded systems, IoT
• Energy-constrained environments
• Research: 1-bit quantization effects

Khi KHÔNG dùng

• Cần full-precision quality → dùng llama.cpp với GGUF
• Cần model lớn hơn (70B+) → hardware constraints vẫn áp dụng

Source

https://github.com/microsoft/BitNet · MIT · official inference framework