Hardware requirements for complex hybrid latent class models in Apollo
Posted: 29 May 2026, 22:34
Dear Apollo Team,
I am currently working with Apollo in the context of hybrid choice modelling, and I have encountered computational limitations when estimating a relatively complex model.
The model is based on a best-worst scaling / maxdiff choice experiment with approximately 6,400 respondents. The specification I would like to estimate includes a latent class structure, two latent variables in the class allocation component, and more than 150 parameters to be estimated. When running the model with parallel processing, I receive the following error message:
Error in checkForRemoteErrors(lapply(cl, recvResult)) :
6 nodes produced errors; first error: cannot allocate vector of size 28.5 Mb
I understand that this is most likely related to memory limitations, especially because the model objects and data may be copied across workers when using multiple cores.
We are now considering purchasing a new computer specifically for estimating such complex Apollo models. Could you please advise what kind of hardware specifications you would recommend, especially in terms of:
- RAM capacity,
- processor type / number of cores,
- whether fewer but more powerful cores or more cores are preferable,
- and whether you would recommend a workstation/server-type machine for models of this complexity?
More generally, I would be grateful for any practical guidance regarding the hardware requirements for estimating large-scale hybrid choice models with latent variables and latent classes in Apollo.
Thank you very much in advance for your help.
Kind regards,
Peter
I am currently working with Apollo in the context of hybrid choice modelling, and I have encountered computational limitations when estimating a relatively complex model.
The model is based on a best-worst scaling / maxdiff choice experiment with approximately 6,400 respondents. The specification I would like to estimate includes a latent class structure, two latent variables in the class allocation component, and more than 150 parameters to be estimated. When running the model with parallel processing, I receive the following error message:
Error in checkForRemoteErrors(lapply(cl, recvResult)) :
6 nodes produced errors; first error: cannot allocate vector of size 28.5 Mb
I understand that this is most likely related to memory limitations, especially because the model objects and data may be copied across workers when using multiple cores.
We are now considering purchasing a new computer specifically for estimating such complex Apollo models. Could you please advise what kind of hardware specifications you would recommend, especially in terms of:
- RAM capacity,
- processor type / number of cores,
- whether fewer but more powerful cores or more cores are preferable,
- and whether you would recommend a workstation/server-type machine for models of this complexity?
More generally, I would be grateful for any practical guidance regarding the hardware requirements for estimating large-scale hybrid choice models with latent variables and latent classes in Apollo.
Thank you very much in advance for your help.
Kind regards,
Peter