Best practices for optimizing PyTorch models with torch.fx include minimizing graph rewrite scope, careful parameter handling when replacing submodules, incremental validation of outputs, thorough profiling with torch.profiler, and modular transformation code. Combining torch.fx with hardware-specific backends boosts performance.