Introducing LLM Quantization Techniques

Herein lies the opportunity for the novel discipline of LLM quantization to provide a more efficient means of scaling AI

Deployment on low-power edge devices, which have less processing power and working memory than cloud-based systems, is problematic because of this

Among the quantization methods Qualcomm study are quantization-aware training (QAT) and post-training quantization (PTQ)

Developers can now choose from a library of more than 100 pre-optimized AI models that are ready to be deployed

A few of the more recent methods made available to the AI community are knowledge distillation and sequential mean squared error

A detailed look into this subject can be found in Qualcomm AI Research’s study on low-rank QAT for LLMs

VQ takes into account the joint distribution of parameters, as opposed to conventional techniques that quantize each parameter separately