While building Lizard, we found that many performance bottlenecks weren't inside the model kernels-they came from memory planning, scheduling, CPU--GPU synchronization, and execution orchestration.
Most AI apps simply load a model.
Lizard plans inference before the first token—optimizing memory, scheduling execution, and running GGUF models locally for faster, more stable AI on Windows.
