Loading content...
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 4080 | $1,199 | 16GB Ada card that balances performance and efficiency. |
| CPU | Ryzen 7 7700X | $299 | Excellent single-core speed for LLM decoding. |
| RAM | 64GB DDR5 | $219 | Future-proof buffer for larger contexts and agents. |
| Motherboard | X670 | $249 | AM5 platform with strong VRMs and expansion lanes. |
| Storage | 2TB NVMe | $149 | Space for multiple quantizations and datasets. |
| PSU | 850W | $129 | Covers RTX 4080 spikes with efficiency. |
| Case | ATX Case | $89 | 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.