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 hold tremendous potential for addressing numerous real-world challenges%2C yet they typically demand significant computational resources and memory. Deploying LLMs onto a resource-limited hardware device with restricted memory capacity presents considerable challenges. Distributed computing emerges as a prevalent strategy to mitigate single-node memory constraints and expedite LLM inference performance. To reduce the hardware limitation burden%2C we proposed an efficient distributed inference optimization solution for LLMs on CPUs. We conduct experiments with the proposed solution on 5th Gen Intel Xeon Scalable Processors%2C and the result shows the time per output token for the LLM with 72B parameter is 140 ms%2Ftoken%2C much faster than the average human reading speed about 200ms per token.?quality=80&w=800)
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