Tell us your budget, what you want to run, and your software setup. We match you to a hand-checked parts list (or a pre-built machine) that runs the AI models you care about.
Set your budget and use case, then hit "Find my build".
You'll get a curated DIY parts list and a matching pre-built machine, side by side.
The AI workstation builder is a wizard that takes a budget, a target workload (general inference, agent / coding, fine-tuning, image / video, multi-GPU lab), and a software stack preference, then matches you to a curated DIY parts list and a pre-built workstation side by side. Each build is hand-checked against the models you intend to run, so the GPU, RAM, PSU, and case all fit together without bottlenecks.
Budgets range from a $1,500 starter rig that handles 13B Q4 models to a $20,000+ multi-GPU lab that can fine-tune 70B in 8-bit. Each build lists the GPU (with VRAM and TDP), CPU, RAM, storage, motherboard, PSU, and case, with itemized prices and direct links to the parts you can buy now. The matching pre-built option is shown alongside so you can decide whether to assemble or buy assembled.
A built-in ROI model runs against your chosen API alternative (OpenAI, Anthropic, Google, or any closed model in the directory) so you see the break-even month before you spend anything. The recommendation is conservative: power draw uses worst-case TDP, electricity defaults to $0.12/kWh, and resale value is ignored, so the real-world payoff usually comes faster than the chart suggests.
Already Have Hardware?
Skip the wizard and detect your existing GPU in the browser. The hardware calculator shows which open-source models fit, at what quantization, and at what tokens-per-second.
Project the break-even between buying this build and paying for a cloud API.
Three-way comparison if you are still deciding whether to buy at all.
Rent the same class of GPU while parts ship or for bursty access.
Browse every GPU, edge device, and AI PC we track with full specs and prices.
Each build is hand-curated, not auto-generated. We pick the GPU first based on the target workload, then balance the rest of the rig around it: enough RAM to mirror VRAM for loading, a PSU sized for GPU TDP plus 30% headroom, a case with proven thermals for that GPU, and storage fast enough to hot-swap models without becoming the bottleneck. Every part is verified compatible with the others.
Build it yourself if you enjoy the process, want the absolute lowest cost, and can troubleshoot. Buy pre-built if your time is worth more than the $300–800 markup, you want a single point of warranty support, or you want the system burned-in before it arrives. Pre-built also wins for workstation-class chassis with 3–4 GPUs since the airflow and PSU sizing get tricky fast.
Yes, around $2,500 to $3,500 for a single-GPU build with a 24 GB or 32 GB card (RTX 4090 or RTX 5090 once retail stabilizes). That budget covers most 30B–70B Q4 models at usable speeds, handles agent workloads comfortably, and pays back inside 12–18 months against frontier APIs at moderate developer usage. Below $1,500 you are restricted to 13B-class models; above $5,000 the ROI curve flattens until you cross into multi-GPU territory.
For inference of large quantized models on a laptop or all-in-one form factor, M4 Max or M3 Ultra Mac Studios with 64–128 GB unified memory are competitive, and they can load models a single NVIDIA card cannot. For raw tokens-per-second, fine-tuning, or anything CUDA-only (most research code), NVIDIA wins. The builder lets you bias toward either platform with the preset.
A single RTX 4090 at 450 W TDP run 8 hours a day costs roughly $13/month at US average electricity rates ($0.12/kWh). A dual-GPU lab can hit $40–60/mo. Important context: most users run inference in bursts, not continuously, so real-world draw is often 30–50% of worst case. Europe at €0.30/kWh roughly doubles these numbers.
Yes for the higher-tier builds: anything with 48 GB+ of VRAM (RTX 6000 Ada, A6000, dual 4090, A100) can fine-tune 7B–13B models comfortably with LoRA or QLoRA. Full fine-tuning of larger models needs multi-GPU with NVLink or rented cloud time, so the builder also points you to the GPU rental price index when the wizard detects a fine-tune workload at a budget that cannot self-host it.
