Building a dedicated local AI workstation? Here are three complete component builds optimized for LLM inference at different budgets.
What Makes a Good Local AI Build?
- GPU VRAM — The #1 priority. More VRAM = bigger models.
- System RAM — 32GB minimum; 64GB if your GPU has <16GB VRAM (for CPU offload)
- Fast NVMe SSD — Models load from disk; fast storage reduces cold-start time
- CPU — Matters less than GPU, but decent CPUs help with CPU offload tasks
- PSU — GPUs are power-hungry; don't under-spec the power supply
Build 1: The Budget AI Starter — ~$500
Target: 7–8B models at good speed. Perfect for students and hobbyists.
| Component | Pick | Price |
|---|---|---|
| GPU | NVIDIA RTX 4060 8GB | ~$280 |
| CPU | AMD Ryzen 5 5600 | ~$100 |
| RAM | 32GB DDR4 3200MHz | ~$55 |
| SSD | 1TB NVMe (WD Blue SN580) | ~$60 |
| PSU | EVGA 650W 80+ Bronze | ~$65 |
| Total | ~$560 |
What it runs: Llama 3.1 8B Q4 at 90–100 t/s, Gemma 3 4B at 120+ t/s, Phi-4 14B (partial)
Build 2: The Performance AI Rig — ~$1,000 ★ RECOMMENDED
Target: 27B models at full speed. The sweet spot for serious local AI work.
| Component | Pick | Price |
|---|---|---|
| GPU | NVIDIA RTX 4060 Ti 16GB | ~$450 |
| CPU | AMD Ryzen 7 7700 | ~$200 |
| RAM | 64GB DDR5 5200MHz | ~$120 |
| SSD | 2TB NVMe (Samsung 990 Pro) | ~$120 |
| PSU | Corsair RM750x 750W 80+ Gold | ~$90 |
| Total | ~$980 |
What it runs: Gemma 3 27B Q4 at 65 t/s, Qwen 2.5 14B at 110 t/s, Llama 4 Scout at 80 t/s, DeepSeek R1 32B (partial)
Build 3: The Power User AI Workstation — ~$2,000
Target: 70B models and Llama 4 at full speed. For professional developers and researchers.
| Component | Pick | Price |
|---|---|---|
| GPU | NVIDIA RTX 4090 24GB | ~$1,600 |
| CPU | AMD Ryzen 9 7900X | ~$280 |
| RAM | 64GB DDR5 6000MHz | ~$150 |
| SSD | 2TB NVMe Gen4 (WD Black SN850X) | ~$130 |
| PSU | be quiet! Straight Power 1000W Platinum | ~$160 |
| Total | ~$2,320 |
What it runs: DeepSeek-R1-Distill-Qwen-32B and Qwen 3 32B comfortably at Q4. Llama 3.3 70B (~43 GB at Q4) and the Llama 4 MoE models (~67 GB / ~242 GB) exceed a single 24 GB card.
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Mini PC Alternative: Mac Mini M4 Pro
Don't want to build a PC? The Mac Mini M4 Pro (24GB unified memory) at $1,399 is a compelling alternative:
- Silent, compact, 30W under AI load (vs 450W for RTX 4090)
- 24GB unified memory = 24GB effective VRAM
- Runs Llama 4 Scout, DeepSeek R1 32B, Gemma 3 27B
- Excellent Ollama support on Apple Silicon
Tips for Buying
- Used GPUs: RTX 3090 24GB can be found for $600–700 used — excellent VRAM-per-dollar
- VRAM > CUDA cores for LLM inference — don't trade VRAM for raw compute
- PCIe 4.0 or 5.0 doesn't meaningfully impact LLM inference performance
- Avoid laptop GPUs — laptop RTX 4090 has only 16GB VRAM vs desktop's 24GB
To size a specific model, use the VRAM calculator: the memory it needs at every quantization and context length.