Local LLM Hardware Recommender Based on Real Benchmarks
Users wanting to run LLMs locally lack clear guidance on which model-hardware combinations actually work well. A benchmark database matching GPU/CPU specs to achievable model performance fills the information gap between hardware purchase and LLM deployment.
Problem Statement
Users buy hardware for local AI inference without knowing actual performance. A 14B model that runs at 40 tok/s on one GPU barely manages 5 tok/s on seemingly similar hardware. No centralized, community-verified source maps hardware to model performance.
The Idea
A recommendation engine that matches your specific hardware (GPU model, VRAM, CPU, RAM) to the best-performing local LLMs with real benchmark data from community submissions.
Why Now
Local LLM deployment is mainstream but performance varies wildly across hardware. Consumer GPU diversity (RTX 3060-5090, M1-M4, AMD) means generic 'minimum requirements' are useless. Community benchmarking culture from PC gaming translates to AI enthusiasts.
Target User
Local AI enthusiasts, developers running self-hosted LLMs, small companies deploying private AI
Target Market
Local AI inference, consumer AI hardware
The full brief is free to read
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- MVP scope & feature boundaries
- Step-by-step validation plan
- Score rationale across 11 dimensions
- Monetization model & pricing angle
- Competitors with links
- Acquisition channels & go-to-market
- Risks & counter-evidence
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