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
FP32 or FP16 full or half precision floating-point numbers are commonly used by LLMs for their weights
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