Which strategy best mitigates dependency solver latency in large Anaconda environments?

Anaconda Navigator and the conda CLI provide a tiered workflow for managing data science environments. While the GUI improves accessibility for new users, the underlying dependency solver can experience significant performance overhead when managing large environments with complex dependency trees.

Maintaining binary compatibility across curated repositories is essential, but the time required to solve environments often increases as the number of installed packages grows. This creates a bottleneck during environment creation and updates.

What are the documented trade-offs between using the default solver and alternative solver configurations to reduce latency? Does the use of specific channel priorities significantly impact the solver's performance in large-scale environments?