As　we　know,　the　training　of　Large　Language　Models　(LLM)　is　time-consuming　and　expensive.　Its　training　efficiency　is　often　affected　by　both　the　heterogeneous　computing　devices　and　heterogeneous　communication　networks　in　the　computing　cluster.　In　recent　years,　new　computing　devices　and　technologies　such　as　NVIDIA　H200　and　Compute　Express　Link　(CXL)　3.0　have　been　proposed,　bringing　new　opportunities　for　improving　the　training　efficiency　of　LLM.　However,　the　actual　deployment　difficulty　and　cost　of　these　new　devices　or　technologies　are　extremely　high,　so　it　is　difficult　for　researchers　to　evaluate　their　impacts　or　improvements　on　LLM　training.　In　order　to　solve　this　problem,　this　paper　introduces　a　simulation　tool　named　HeterSim,　and　proposes　to　simulate　and　evaluate　LLM　training　in　CXL-based　heterogeneous　computing　clusters　using　HeterSim.　This　article　takes　the　LLM　called　LLaMA　as　a　simulation　example,　and　successfully　simulates　and　analyzes　the　impact　of　heterogeneous　computing　and　CXL　technologies　on　LLM　training.　We　hope　that　this　article　can　provide　researchers　with　new　ideas　for　simulating　and　analyzing　LLM　training,　and　help　researchers　explore　the　impact　of　emerging　technologies　on　LLM　training　at　low　cost.　©　2024　IEEE.