LunaVox is a high-performance, cross-platform text-to-speech inference engine specialized for Qwen3-TTS models. It provides a unified C++ core with Python bindings, offering multiple interfaces: a CLI (`lunavox`), a desktop GUI (customtkinter), an HTTP/WebSocket server (FastAPI), and a Python runtime API. The architecture separates model loading, tokenization, LLM-based talker/predictor generation (via llama.cpp), and neural audio codec decoding (via ONNX Runtime) into modular components. Voice modes include base, voice cloning from reference audio, custom speaker selection, and text-described voice design. The system is designed for low-latency streaming synthesis, with benchmarks showing real-time factors below 0.2 on consumer GPUs. It includes a comprehensive model conversion pipeline from HuggingFace to GGUF/ONNX formats, a build toolchain for compiling the C++ engine, and production-ready serving with Prometheus metrics and auto-batching. The codebase demonstrates strong engineering practices with cross-platform support, structured stats contracts, and bilingual documentation, though it carries some technical debt from vendored GGUF code and platform-specific workarounds.