ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference
A new quantization method cuts model size by 4x without sacrificing accuracy for reasoning tasks
Deep Dive
ParoQuant is a project available on Z-Lab's website, GitHub, and Hugging Face.
Key Points
- Reduces LLM memory footprint by up to 4x using 2-bit pairwise rotation quantization.
- Boosts inference throughput by 2.5x on reasoning benchmarks like GSM8K and MATH.
- Open-source release on GitHub and Hugging Face, compatible with vLLM and llama.cpp.
Why It Matters
Cuts LLM inference costs and latency for reasoning tasks, enabling enterprise deployments on limited hardware.