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A parts list optimized for running 7B–13B LLMs locally while staying quiet and efficient. Upgrade the GPU and RAM when you scale into 70B territory.
Upgrade path: swap GPU to RTX 4090 + bump RAM to 64GB when ready for 70B models.
| Component | Part | Price | Why we picked it |
|---|---|---|---|
| GPU | RTX 4070 Ti | $799 | Best value Ada GPU for 7B–13B workloads. |
| CPU | Ryzen 7 5700X | $199 | Affordable 8-core chip that pairs well with mid-tier GPUs. |
| RAM | 32GB DDR4 | $89 | Enough memory for inference stack plus monitoring tools. |
| Motherboard | B550 | $129 | Mature AM4 board with PCIe 4.0 for fast NVMe. |
| Storage | 1TB NVMe | $79 | Fast storage for models with quick load times. |
| PSU | 750W | $99 | Gold-rated unit sized for mid-range GPUs. |
| Case | ATX Case | $69 | Standard chassis with decent airflow and space. |
Yes. The PSU and case handle up to an RTX 4090 (you'll just need PCIe 5.0 adapters and stronger airflow).
For pure inference workloads, DDR4 is fine. Upgrade to AM5/DDR5 when you move beyond 13B models or need PCIe 5.0 storage.
Start by bumping to a 24GB GPU (RTX 4090) and increasing RAM to 64GB for heavier multitasking.
Expect ~420W under inference load and ~80W at idle. A 750W PSU provides 30% headroom.
RTX 4080 platform for faster 13B–70B experimentation.
RTX 4090 workstation ready for production 70B workloads.