Rust ML Framework Candle is a minimalist machine learning framework designed for Rust, emphasizing performance, including GPU support, and ease of use. It aims to make serverless inference possible by deploying lightweight binaries, which is faster than full frameworks like PyTorch. Candle also allows for Python-free production workloads, addressing the performance issues caused by Python overhead and the Global Interpreter Lock (GIL). The framework is part of the Hugging Face ecosystem, with Rust crates like safetensors and tokenizers. Users can run matrix multiplication examples, utilize CUDA for GPU acceleration, and explore browser-based demos. Issues like missing symbols during compilation or permission for LLaMA-v2 model are addressed with specific solutions. Candle’s documentation provides a cheatsheet and guides for troubleshooting common errors, such as those related to the MKL library or the Cuda compiler. The framework also supports WebAssembly examples and command-line examples using state-of-the-art models. Candle’s core goal is to facilitate efficient and scalable machine learning deployments.