Best gaming setup for running local LLMs in Witbank

Quick Answer

A Witbank setup for running local LLMs depends on GPU VRAM first and system RAM second. An RTX 4070 (12GB) covers many quantised models; 16GB or 24GB runs larger ones faster. Pair with 32GB to 64GB RAM and a 1TB-plus NVMe SSD, with the tower around R28,000 to R55,000. Test a smaller quantised model first to confirm your workflow before downloading huge files. Plan for at least 1TB of SSD space, since models range up to tens of gigabytes each.

VRAM First, Speed Second

For local language models, VRAM sets the ceiling on model size and the GPU's power sets the response speed. A 12GB card runs quantised 7B to 13B models well; more VRAM lets larger models stay entirely on the GPU, which is far faster than offloading. NVIDIA cards are the reliable pick given the CUDA-based tooling most runtimes depend on.

Memory And Storage Strategy

System RAM of 32GB to 64GB lets you load models that exceed VRAM, trading speed for size. A 1TB or larger NVMe SSD stores several big model files and loads them quickly, which matters since they range up to tens of gigabytes. Size the PSU to the GPU, and keep the CPU a sensible mid-range chip to feed the card.

FAQ

Should I prioritise VRAM or GPU speed?

VRAM first, since it determines which models you can run at all. Among cards with enough VRAM, faster ones then give quicker token generation.

How much SSD space do local models need?

Plan for at least 1TB, since individual models can be several to tens of gigabytes. More space helps if you keep a library of different models on hand.

Is a local LLM private?

Yes, running a model locally keeps your prompts and data on your own hardware with no external server involved, which is a key reason people run them at home.