QIGen: Generating Efficient Kernels for Quantized Inference on Large
  Language Models
Tommaso Pegolotti Elias Frantar Dan Alistarh Markus Püschel
We present ongoing work on a new automatic code generation approach for
supporting quantized generative inference on LLMs such as LLaMA or OPT on
off-the-shelf CPUs. Our approach is informed by the target architecture and a
performance model, including both hardware characteristics and method-specific
accuracy constraints. Results on CPU-based inference for LLaMA models show that
our approach can lead to high performance and high accuracy, comparing
favorably to the best existing open-source solution. A preliminary
implementation is available at https://github.com/IST-DASLab/QIGen.