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%20hold%20tremendous%20potential%20for%20addressing%20numerous%20real-world%20challenges%2C%20yet%20they%20typically%20demand%20significant%20computational%20resources%20and%20memory.%20Deploying%20LLMs%20onto%20a%20resource-limited%20hardware%20device%20with%20restricted%20memory%20capacity%20presents%20considerable%20challenges.%20Distributed%20computing%20emerges%20as%20a%20prevalent%20strategy%20to%20mitigate%20single-node%20memory%20constraints%20and%20expedite%20LLM%20inference%20performance.%20To%20reduce%20the%20hardware%20limitation%20burden%2C%20we%20proposed%20an%20efficient%20distributed%20inference%20optimization%20solution%20for%20LLMs%20on%20CPUs.%20We%20conduct%20experiments%20with%20the%20proposed%20solution%20on%205th%20Gen%20Intel%20Xeon%20Scalable%20Processors%2C%20and%20the%20result%20shows%20the%20time%20per%20output%20token%20for%20the%20LLM%20with%2072B%20parameter%20is%20140%20ms%2Ftoken%2C%20much%20faster%20than%20the%20average%20human%20reading%20speed%20about%20200ms%20per%20token.)













































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