Planning a $5,000–$50,000 AI hardware purchase? Get the architecture reviewed first.
Local LLMs, training, inference, rendering, and generative workloads need very specific hardware decisions — VRAM ceilings, PCIe lanes, cooling envelopes, and software stacks. We help you make them before you order parts.
Local LLM workstation planning
Sizing the machine to the models you actually want to run today and 12 months from now.
GPU and VRAM selection
VRAM is usually the constraint that matters most. We help you reason about quantization, model size, batch size, and context length.
Multi-GPU considerations
- NVLink vs PCIe trade-offs
- Power and cooling for 2–4 GPUs
- Motherboard and PSU compatibility
- Driver and CUDA setup
CPU, motherboard, and PCIe lane planning
Lane count drives multi-GPU and high-throughput NVMe more than most buyers expect.
Cooling, airflow, and noise
Air vs hybrid vs custom loop — chosen for the room you're putting the machine in.
Power supply sizing
Sustained load, transient spikes, and clean headroom — sized once, correctly.
Storage throughput
Dataset drives, scratch space, and backup tiers planned for the workload, not just capacity.
Linux vs Windows, drivers, and CUDA
Pragmatic OS choice based on the framework, the toolchain, and your day-to-day.
AI software stack guidance
PyTorch, vLLM, llama.cpp, ComfyUI, Ollama — what to install, where, and why.
Rackmount vs tower, and office placement
Form factor, heat output, and noise budget for the actual room.
Budget tiers
Workstation tiers from single-GPU local dev rigs to four-GPU training boxes — with honest performance expectations.
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Common Questions
Plan it before you buy it.
Tell us what you're trying to build. We'll review the project and reply within one business day.