The article argues against using Python for inference applications, citing issues such as complex dependencies, large size, slow speed, and difficulty in porting across devices. It further highlights the benefits of Rust+Wasm, including being ultra-lightweight, fast, portable, easy to set up, develop and deploy, and safe for cloud-ready applications. The article concludes by inviting contributions to the open-source projects and discussing the potential of WasmEdge and WASI NN in building inference applications for popular AI models beyond LLMs.
Key takeaways:
- The Rust+Wasm stack provides a strong alternative to Python for AI inference, offering benefits such as smaller app size, faster speed, and secure running on all hardware accelerators without any change to the binary code.
- A simple Rust program was created to run inference on llama2 models at native speed, and when compiled to Wasm, the binary application is completely portable across devices with different hardware accelerators.
- The WasmEdge runtime provides a safe and secure execution environment for cloud environments, working seamlessly with container tools to execute the portable application across various devices.
- The Rust+Wasm stack is ultra-lightweight, very fast, portable, easy to set up, develop and deploy, safe and cloud-ready, making it a strong alternative to the Python stack for AI inference applications.