SqueezeLLM Inference With SYCLomatic

Turn on SqueezeLLM for Efficient LLM Inference on Intel Data Center GPU Max Series utilizing SYCLomatic for Converting CUDA to SYCL.

Researchers at the University of California, Berkeley, have devised a revolutionary quantization technique called SqueezeLLM

Using the SYCLomatic tool from the Intel oneAPI Base Toolkit to take advantage of CUDA-to-SYCL migration

SqueezeLLM is a tool that UC Berkeley researchers have created to facilitate precise and efficient low-precision quantization

Non-uniform quantization is used by SqueezeLLM to best represent the LLM weights with less accuracy

With SYCLomatic's CUDA-to-SYCL code translation, effective kernel strategies may be decoupled from the target deployment platform

SqueezeLLM allows for low-precision quantization to provide accurate and efficient generative LLM inference

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