Perplexity Open Sources Lily: A Rust + Metal Inference Engine for Qwen3.6-35B-A3B on Apple Silicon
Perplexity has open sourced Lily, the local inference engine behind Hybrid Compute in Perplexity Computer. It is a single-process runtime: a Rust layer loads the checkpoint and drives the generation loop, an OpenAI-compatible chat-completions API streams tokens, and hand-written Metal kernels execute the model. Neither PyTorch nor MLX sits in the execution path. Lily is deliberately narrow with one model, Qwen3.6-35B-A3B, on one hardware family and that narrowness is the performance argument.
Perplexity has released Lily, a specialized software engine written in Rust and Metal that runs a specific AI model on Apple computers without using standard frameworks like PyTorch or MLX. The engine is designed narrowly for one model and one hardware family, which allows it to execute faster than existing general-purpose solutions, achieving roughly 1.2 to 1.35 times better performance in testing. This matters because it demonstrates how custom-built inference engines tailored to specific constraints can outperform flexible, reusable frameworks by optimizing every layer from weight loading through token generation. The code is publicly available, though the hardware requirements are substantial: an Apple silicon Mac with at least 32 GB of unified memory is needed to run the model.
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