Fine-Tuning AI Models Locally in Uganda: Hardware Requirements Explained

Fine-tuning sits between inference and full training

Running a model (inference) needs the least VRAM. Fine-tuning needs more, because you're computing and storing gradients during training, not just running the model forward. Training a model completely from scratch needs the most, and is genuinely out of reach for almost all local hardware, Ugandan or otherwise.

Techniques that make local fine-tuning realistic

• LoRA / QLoRA -- these methods fine-tune a small set of additional parameters instead of the whole model, dramatically cutting VRAM needs. A 12-16GB card can meaningfully fine-tune 7-13B models this way.
• Quantization during fine-tuning -- combined with LoRA, this is what makes fine-tuning genuinely accessible on consumer GPUs rather than requiring a data-center card.

What you actually need

Storage matters more than people expect

Fine-tuning datasets, checkpoints and model versions add up fast -- budget for a fast NVMe SSD with real headroom (1TB+), not just enough space for the OS and software.

Realistic expectations for Ugandan researchers and businesses

You can genuinely fine-tune useful, specialised models locally on a single strong consumer GPU using LoRA-style methods -- this covers most practical business use cases (customer support tuning, local language adaptation, domain-specific assistants) without needing cloud compute budgets.