Depends on game/environment and—since it's using a GBDT and not a NN—how good you are at feature extraction/selection for your problem.
High level, I'd say it's a good way to test a new environment w/out spending time/effort on GPUs until you understand the problem well, and then you can switch to the time/money costly GPU world.
Chasing pointers in the MCTS tree is definitely a slow approach. Although typically there are ~ 900 "considerations" per move for alphazero. I've found getting value/policy predictions from a neural network (or GBDT[1]) for the node expansions during those considerations is at least an order of magnitude slower than the MCTS tree-hopping logic.