AI Training Workstations in Uganda: Local GPU Rigs for ML

Why VRAM is the number that matters most

For AI training and fine-tuning, GPU VRAM capacity is usually the real limit on what model sizes and batch sizes you can work with locally -- more VRAM lets you train larger models or use bigger batch sizes before running into out-of-memory errors, which matters more than raw clock speed for most training workloads.

• RTX 4090 (24GB VRAM): strong default for fine-tuning mid-size models and most computer-vision and NLP work
• RTX 5080/5090 (16-32GB VRAM): the current ceiling for local single-GPU training and larger fine-tuning jobs
• RAM: 64-128GB depending on dataset size -- data loading and preprocessing lean on system RAM as much as the GPU
• Storage: fast NVMe SSD, since dataset I/O speed directly affects training throughput

Built for Uganda's growing AI community

With Makerere's new AI Research Cloud and programmes like IndabaX Uganda and the AI Innovation Academy training a fast-growing community of local ML practitioners, having a capable machine at home or in the lab matters for the work that happens between cloud sessions -- prototyping, local fine-tuning, and inference without a running cloud bill. We build and stress-test these rigs under real training loads, not just idle benchmarks, before they leave Kampala.