AI performance is a property of the system, not the chip.

LynxBenchAI is a benchmarking methodology — and, at launch, a corresponding set of results — for AI hardware. It measures performance as a property of the complete hardware-and-software stack, sustained under realistic load, reported per precision, with bounded optimisation. The point is to inform real procurement and deployment decisions, not to crown a winner.

Methodology has to be settled before any number lands — otherwise the number means whatever the reader assumes. Publishing the framing first means that, when results arrive, what they measure (and what they don't) is unambiguous. Why methodology defines what you can compare →

The AI Executor is the hardware-and-software pair that actually runs the workload — the GPU together with its driver, runtime, framework, and kernel implementations. Performance is a property of this pair, not of the silicon alone, and the same chip under a different stack can perform very differently. Read the article →

Three differences at the method level. First, we publish sustained throughput, not peak. Second, every result is reported per precision, with no collapsed aggregate score. Third, every entrant is tuned under a declared, bounded optimisation budget, so two numbers compare on equal footing. The aim is procurement-grade comparability, not a leaderboard. Why benchmarks mislead procurement →

Buyers and platform engineers who need defensible evidence for AI hardware decisions, procurement reviewers who need a comparable basis between systems, and the technical leadership inside vendors who want their products evaluated on the load they were designed for, with the trade-offs visible. Read the decision framework →

A precise launch date has not been published. The methodology is complete; the next phase attaches empirical artefacts to it. Ask to be notified at launch →