Show HN: A visual explainer tracing an LLM API request to the GPU and back (github.com)

A new project has been launched that visually tracks an API request to a large language model (LLM) from initial code execution to GPU processing and back. This initiative provides a detailed explainer video, complete with 2D and 3D renderings, aimed at developers interested in understanding the intricate steps involved in LLM API requests, such as caching, batching, and speculative decoding. The project utilizes tools like Node.js, ffmpeg, and Playwright to create frame-by-frame video captures of the process, making it accessible for a wide range of users. This visual representation is significant for the AI/ML community as it demystifies the often opaque process of LLM API calls, showcasing the performance bottlenecks and time distribution at each stage. By using these visuals, developers can more effectively optimize their API requests, leading to improved efficiency in deploying AI applications. The project emphasizes collaborative coding through features that allow for iterative feedback and adjustments, ultimately enhancing the learning experience for engineers and researchers alike.