Best gaming setup for running local LLMs in Secunda

Quick Answer

For running local LLMs in Secunda, GPU VRAM is the deciding spec: a 12GB RTX 4070 runs quantised 7B-13B models like Llama 3 8B smoothly, while 16GB or 24GB cards handle 30B-class models. Pair it with a Ryzen 7 7700 and 32GB DDR5; budget R25,000-R40,000 depending on the GPU.

VRAM is the gatekeeper

Local language models are bound by GPU memory, not frame rates. A 12GB RTX 4070 runs quantised 7B-13B models such as Llama 3 8B and Mistral at good token speeds. Running 30B-class models or long context windows needs 16GB (RTX 4070 Ti Super) or 24GB. 32GB DDR5 system RAM enables CPU offloading when a model overflows the card.

Storage, RAM and software

Use a 1TB NVMe minimum; a quantised 13B model can be 8-15GB and a model library fills space fast. 32GB DDR5 supports offloading and running Ollama or LM Studio with a model loaded. A high-airflow case keeps the GPU stable through long inference sessions.

Secunda delivery and support

Evetech delivers to Secunda within roughly 3-5 working days in the Mpumalanga courier zone, with the system under warranty so support is handled without travelling. That cover is valuable on a build where the high-VRAM GPU is the main expense.

FAQ

How fast are local LLMs on a 12GB card?

Quantised 7B-13B models run at conversational speeds on a 12GB RTX 4070, fast enough for chat, coding help and document tasks.

Do I need an internet line to run them?

Only for the initial model download. After that, inference runs fully offline on your NVMe with no further data use.

Is 32GB RAM necessary?

It is strongly recommended. 32GB DDR5 enables offloading larger models to system memory and keeps the OS and tools responsive during inference, which matters if you run a browser or IDE alongside the model.