1 link tagged with all of: liquid ai + inference + benchmarking + speculative decoding + quantization
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Liquid AI released Pipette, an open-source benchmarking suite for measuring AI model performance on phones, laptops, and embedded devices, plus DSpark draft models that speed up inference by 2-3x through speculative decoding. Both tools come with public datasets and are designed to let developers test the exact hardware-model-runtime combinations they plan to ship.
- Pipette provides a standardized way to benchmark 35+ model classes across different quantization levels, runtimes, and devices (phones, laptops, PCs, embedded hardware), with 10k+ verified results already in the warehouse and an interactive dashboard to compare configurations.
- DSpark draft models achieve 2.27x to 3.18x throughput gains on everything from M4 Max MacBooks to H100 GPUs by using lightweight draft models to propose token blocks that a target model verifies in one pass, without changing output quality.
- Both projects are open-source with native iOS and Android apps, public datasets, and submission workflows that let developers contribute benchmarks for their own hardware and model combinations.