Liquid AI launches Pipette benchmark suite

Pipette addresses a major blind spot in AI benchmarking: cloud scores rarely predict how models behave on phones, laptops, or other local hardware.

• –Public results help developers choose practical model-and-device combinations
• –Deterministic task-specific scorers avoid relying entirely on opaque LLM judges
• –Tracking quantization, runtime, context length, and memory exposes real deployment tradeoffs
• –The leaderboard could become valuable infrastructure for local AI, though coverage and reproducibility will depend on broader device participation
• –Early results should be treated as configuration-specific, since thermals, software stacks, and hardware differences can materially change performance