How Much VRAM Do You Need to Run Local LLMs in Uganda?

VRAM is the hard limit, not GPU speed

A language model has to fit in your GPU's VRAM to run efficiently. If it doesn't fit, it either won't load at all, or it spills over into system RAM and slows down dramatically -- often 10x slower or worse. This makes VRAM capacity, not GPU speed, the number one thing to plan around.

Rough VRAM requirements by model size (quantized)

These figures assume quantized (compressed) models, which is standard practice for local inference -- running the full-precision version of these models needs roughly 2-4x more VRAM.

What's realistic for most people in Uganda right now

For learning, prototyping and running genuinely useful assistants locally, a 12GB card comfortably runs excellent 7B-13B models with good quality. That's the sweet spot for most students, developers and small businesses experimenting with local AI -- it doesn't require the most expensive card on the market.

When to just use the cloud instead

If you need a 70B-class model or need to run it for many concurrent users, local hardware in Uganda quickly becomes more expensive than renting cloud GPU time for those specific jobs. Local hardware wins for privacy, latency and predictable monthly cost at the smaller end; cloud wins for occasional large-scale runs.

Our advice

Start with a 12GB card, get comfortable with quantized 7B-13B models, and only scale up hardware once you've confirmed the exact model size your project actually needs.