The growth of AI and machine learning in Nigeria is creating real demand for local training infrastructure. While cloud compute (AWS, GCP, Azure) works for production, local workstations for experimentation, fine-tuning, and inference are increasingly cost-effective.
VRAM Is King for ML
GPU VRAM is the single most important spec for ML work. Models and their activation tensors must fit in GPU memory. Modern large language models (LLMs) in quantised form require 8–24GB VRAM for inference. Training requires more. An RTX 4090 (24GB) handles most fine-tuning tasks. The RTX 5090 (32GB) is the current consumer champion for local ML.
CPU: Supporting Role
For pure deep learning, the CPU manages data loading and preprocessing. A modern 8–12 core CPU (Ryzen 7 7700X, Core i7-13700K) is more than sufficient. Don't spend extra CPU budget here — put it into VRAM.
System RAM for ML
Model checkpoints, datasets, and data pipelines live in system RAM. 64GB is the practical minimum for serious ML work; 128GB is recommended if you work with large datasets that won't fit in a database. DDR5 bandwidth benefits data-loading throughput.
Storage Strategy for Datasets
ML datasets can be enormous — tens to hundreds of gigabytes. Fast NVMe storage for active datasets matters: a slow drive creates a data-loading bottleneck that keeps the GPU idle. A 2TB PCIe 4.0 NVMe for active work, plus a large HDD or second NVMe for archived datasets.
Recommended ML Workstation
- CPU: AMD Ryzen 7 7700X
- RAM: 128GB DDR5
- GPU: NVIDIA RTX 4090 24GB (primary option) or RTX 5090 32GB (maximum)
- Storage: 2TB PCIe 4.0 NVMe + 4TB HDD
- PSU: 1000W 80+ Platinum (RTX 4090 needs headroom)
- Cooling: 360mm AIO — high-load compute runs hot
Sephora Systems AI Series builds are purpose-configured for this use case.