Threadripper Pro + Dual RTX 4090 Build

This is the definitive ceiling for local workstation AI in this index. Two RTX 4090s provide 48GB of combined VRAM — the amount needed for reliable 70B-parameter inference at 4-bit precision — behind a Threadripper Pro with the PCIe lanes to feed both cards and 256GB of system RAM. It runs models in the Llama 3.1 405B and gpt-oss-120b class at roughly 10–20 tokens/second.

What it removes

The ongoing cost of cloud compute for heavy, continuous production workloads — fine-tuning runs, batch inference, rendering pipelines that never idle. At that usage level, the capital cost amortises against an API bill that would otherwise never stop.

The trade-offs

• Power. Peaks above 1500W. This needs a dedicated circuit and a real PSU, and it will heat the room.
• Noise. Under sustained load it is loud enough that it belongs in another room, not on your desk.
• Thermal management is not optional — airflow and case choice make or break stability here.

Who should buy

Teams fine-tuning 70B models on-premises, studios running continuous 3D pipelines, and anyone whose cloud-compute bill has crossed the line where owning the hardware is cheaper. For intermittent inference, a single 16–24GB card is the far more sensible buy.