Local LLM Hardware Advisor

Help developers pick the right hardware and model combo for local AI workloads

Problem

Developers want to build local AI setups for startup use cases like cleanup, log summaries, and routine tasks, but with options like AMD Strix Halo, NVIDIA, and Apple Silicon, they don't know which offers the best value, what models it can run, and where it falls short; online information is scattered and lacks workload-specific recommendations.

Solution

An interactive advisory tool: users input budget, primary use (inference/fine-tuning/batch), model size, privacy needs, etc., and the tool outputs hardware suggestions (CPU/GPU/RAM/storage), expected performance (tokens/s), a list of runnable models, and caveats; also includes community-shared real-world benchmarks.

Users

Developers, small teams, and privacy-sensitive companies needing local LLM deployment

MVP

Build a web app with a hardware database and model performance estimator; users fill a form to get recommended configs; show community-submitted benchmarks (e.g., tokens/s for Llama 3 8B on various hardware); allow users to submit their own setups and results

Revenue

Free basic recommendations + paid detailed reports or 1:1 consulting; affiliate links to hardware; custom enterprise selection services

Why now

Local LLM demand is growing, but hardware iterates fast and choices are many; developers need neutral, practical guidance; existing content is often outdated or vendor-sponsored, so an indie developer can build trust with objective data