EMalahleni-based gamers: what running local LLMs hardware actually delivers

Running a language model on your own machine is mostly a question of how much GPU memory you have. For eMalahleni buyers who want private, offline AI alongside gaming, the VRAM number on the box decides which models you can load at all.

Quick Answer

To run 7B and 13B models comfortably with quantisation, get a graphics card with at least 12GB of VRAM, 32GB of system RAM and a Ryzen 5 or Core i5, costing roughly R25,000 to R30,000. A 16GB card opens up larger models and longer context windows if the budget stretches.

VRAM Is The Gate, Not The CPU

A quantised 7B model fits in about 6GB, a 13B in roughly 10GB, so a 12GB card runs both with room for context. Models that spill over into system RAM slow to a crawl, dropping from dozens of tokens per second to a painful trickle, so buy the most VRAM you can rather than the fastest core.

System RAM And Storage Still Matter

Keep 32GB of system memory so the OS, your browser and the model loader coexist without thrashing. Model weights are large, often 4GB to 15GB each, so a 1TB NVMe SSD lets you keep several downloaded and swap between them quickly instead of re-downloading over a metered line.

The Gaming Bonus

A 12GB or 16GB card that runs local models is also a strong 1440p gaming GPU, pushing well over 100fps in competitive titles. That dual purpose is why an AI-capable build rarely feels like wasted money, since the same silicon entertains you between inference sessions.

FAQ

How much VRAM do I need to run a useful local model?

12GB lets you run quantised 7B and 13B models with a decent context window. If you want 30B-class models, look at 24GB cards, though those push the budget well past R40,000.

Does the CPU matter for local AI?

Less than the GPU. The CPU handles prompt processing and tokenisation, so a modern 6-core chip is plenty; the graphics card does the heavy lifting during generation.

Can I run models without a dedicated GPU?

You can on CPU and system RAM, but speed drops sharply to a few tokens per second. A discrete GPU with 12GB is the practical minimum for responsive, conversational use.