Local AI and Server Infrastructure

Local LLM Hardware Readiness Checker

Live now

Evaluates whether your current hardware can run a useful local AI model — covering VRAM, RAM, CPU requirements, and model size fit — so you know what is realistic before spending time or money on setup.

Interactive tool Structured result Recommended next steps

Best for

Developers and technically capable users who want to run AI models locally for privacy or cost reasons and need to know whether their hardware is sufficient before starting.

Who this tool is for

Developers and technically capable users who want to run AI models locally for privacy or cost reasons and need to know whether their hardware is sufficient before starting.

What this tool checks

GPU VRAM availability and whether it is enough for the model sizes you want to run
System RAM — critical for CPU-only inference or for models that overflow VRAM
CPU capability for models that run without a GPU or use partial GPU offloading
Compatibility between your hardware and the most common local AI runtimes (Ollama, llama.cpp, LM Studio)

What you will get

A readiness verdict — ready, marginal, or not suitable — with a plain-language explanation
A list of model sizes and types your hardware can realistically run well
Specific upgrade recommendations if your hardware falls short, ranked by cost-effectiveness
A practical first-stack direction including runtime and interface recommendations

Run the tool

Enter your details and the tool will return a structured result based on your inputs.

Need implementation help?

Need help turning the hardware assessment into a working private AI setup?

Local AI delivers real value only after the runtime, model selection, interface, retrieval layer, and privacy workflow are configured together. Use the contact form to get help building a complete private AI stack.