Building a Home AI Lab in Uganda: What It Actually Costs

Tier 1: Learning and experimentation

A capable entry point for running local LLMs, learning frameworks like PyTorch, and experimenting with Stable Diffusion. Ryzen 5/Core i5, 32GB RAM, RTX 4060 Ti (16GB) or RTX 4070 (12GB). This tier genuinely covers most hobbyist and student-level AI work.

Rough cost: UGX 5,500,000 - 7,500,000

Tier 2: Serious fine-tuning and local research

Enough headroom for LoRA fine-tuning larger models, running bigger local LLMs comfortably, and faster iteration on real projects. Ryzen 7/Core i7, 64GB RAM, RTX 4090 (24GB).

Rough cost: UGX 11,000,000 - 15,000,000

Tier 3: Multi-model, production-adjacent work

For running multiple models simultaneously, larger-scale fine-tuning, or supporting a small team's AI experimentation. Ryzen 9/Core i9, 128GB RAM, RTX 5090 (32GB) -- possibly with a second GPU down the line.

Rough cost: UGX 20,000,000+

What actually matters most as you go up in tier

The jump from Tier 1 to Tier 2 is mostly about VRAM headroom and RAM for handling bigger datasets and models without workarounds. The jump from Tier 2 to Tier 3 is often more about supporting multiple simultaneous workloads (a team, or multiple projects) than about any single task needing dramatically more power.

Start smaller than you think you need

Most people significantly overestimate the tier they need on day one. Start at Tier 1, actually use it for a few months, and let real bottlenecks -- not speculation -- tell you when it's time to upgrade.