Running the same model on different machines can result in different solution paths.
This effect is a well-known phenomenon called "Performance Variability". Using different hardware plays an important role as the search path within the branch-and-bound tree can be significantly different. In addition, changing the order of variables or constraints or using a different random seed can drastically impact the performance even when the mathematical models are identical. This is described in this MIPLIB 2010 paper.
The reasons for performance variability are quite diverse. Gurobi makes many decisions in the search for an optimal solution. Whenever there is a decision that is a tie or even close to a tie, a difference in the computing environment, even if it is a seemingly insignificant, can lead to a different outcome. These differences can originate from a different operating system, a different underlying library, different computer hardware, etc.
Gurobi tries to exploit this when running in ConcurrentMIP mode.
To get a sense of how susceptible your model is in regards to performance variability, you can compare the performance of several runs that only differ in the random seed parameter.