Large Language Models (LLMs) have gained popularity for their text generation and language understanding capabilities. However, their adoption is challenging due to the large memory requirements. Intel researchers propose using quantization methods to reduce computational power on CPUs. Their approach includes INT-4 weight-only quantization and a specialized LLM runtime for efficient inference. Experimental results show comparable accuracy to FP32 models and improved latency performance. The researchers plan to further enhance generative AI on PCs to meet increasing demands.
Intel Researchers Propose a New Artificial Intelligence Approach to Deploy LLMs on CPUs More Efficiently
Large Language Models (LLMs) have gained popularity for their remarkable performance in tasks such as text generation and language understanding. However, their large model size requires specialized hardware for deployment, making it challenging for many organizations.
Intel researchers have developed a solution to efficiently deploy LLMs on CPUs. They use quantization methods to reduce the computational power required for inference. This includes INT8 and weight-only quantization, which optimize the precision of weights and activation functions in the neural network.
The researchers have also designed a specialized LLM runtime that accelerates the inference process on CPUs. They have tested the performance of the quantized models on popular LLMs and observed that the accuracy is comparable to the original models. Additionally, the LLM runtime outperforms other solutions in terms of latency.
This research paper provides a solution to the challenge of deploying LLMs on CPUs, which were traditionally limited to specialized hardware like GPUs. The proposed approach allows for efficient inference on CPUs, enabling more organizations to leverage LLMs. The method demonstrated comparable accuracy to the original models and outperformed other solutions. Further improvements are planned to meet the growing demands of AI-generated content.
For more details, you can check out the research paper and the Github repository.
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