WhatA tool that predicts real-world LLM inference performance for specific model/hardware combinations before you buy, accounting for prefill, decode, context scaling, and thinking-token overhead.
SignalUsers running local inference on hardware like DGX Spark and Mac Ultra are discovering that performance varies dramatically by use case — a system great for short-output tasks can be unusable for long-generation workloads — and there is no way to predict this before committing to expensive hardware.
Why NowThe proliferation of consumer and prosumer AI hardware (Mac Ultra, DGX Spark, RTX 5090) combined with thinking models that multiply token requirements has made hardware selection a high-stakes decision with poor information.
MarketAI developers and companies purchasing inference hardware ($10B+ AI hardware market); no direct competitor offers use-case-specific performance prediction; current benchmarks like LMSys focus on quality not speed-per-dollar.
MoatA proprietary database of validated hardware/model/use-case performance triples that grows with community contributions, creating a data moat competitors cannot easily replicate.
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