Yetunde is a data scientist at a Lagos fintech company. For most of her work — pandas dataframes, sklearn models, exploratory analysis — her laptop was sufficient. But when it came to heavier tasks — training XGBoost on millions of rows, running SHAP explanations on complex models, or doing anything that required GPU acceleration with PyTorch — she defaulted to Google Colab.
Colab is free up to a point. But session disconnections, memory limits, and the latency of uploading datasets to the cloud had become a constant friction in her workflow. She needed a local machine that could handle the full range of her workload.
The Build
After discussing her actual workload, we built:
- AMD Ryzen 9 7950X (16 cores — pandas operations, cross-validation loops, and parallel feature engineering all use CPU cores)
- 128GB DDR5 RAM (entire datasets in memory — no sampling)
- NVIDIA RTX 4070 Ti Super 16GB (CUDA for PyTorch, RAPIDS for GPU-accelerated dataframes)
- 2TB NVMe PCIe 5.0 (fast I/O for large CSV and Parquet file reads)
- Windows 11 Pro with WSL2 (full Linux environment within Windows for her Python toolchain)
What Changed
Jobs that ran for 20 minutes on Colab now complete in under 4 minutes locally. She works with her full dataset, not samples. No session timeouts. And the work stays in her environment — no cloud uploads, no privacy considerations around client data.