Hyperparameter tuning approach question [R]

I am doing some work with cell type classification, where I have 4.3 million cells and 512 features (condensed embeddings from the encoder of a transformer).

The broader goal is to implement a contextual bandit for augmenting the training set of the dataset, as it is currently imbalanced, and rare cell type classification is poor when I tried a baseline logistic regression classifier.

Dataset:
Feature matrix shape: (4290471, 512)
Labels shape: (4290471,)

Class distribution:
T cell 1966941
DC 858451
NK cell 561904
Monocyte 411170
B cell 375882
Platelet 54576
Progenitor cell 24689
ILC 24254
Erythrocyte 12604

I didn't do any hyperparameter tuning for the LR classifier, but I want to try other ML models (LightGBM, XGBoost, SVM)

However, I face a bottleneck with hyperparameter tuning. I want to do 80/10/10 train/validate/test split, but the training set is so large and takes a long time even on H100.

What are some solutions to this? I tried optuna but still very long for each hyperparameter trial. I then tried optuna but instead of using the full 80% for training each time, only 15% of the 80% is used (subsampling from the training set). I'm not sure if this is robust or not. I also couldn't really find anything in the literature.

Anyone been in a similar situation?