C++ Vs Rust: Which is better for writing AI/ML code with LLMs (richiejp.com)

A recent exploration into the use of C++ versus Rust for AI/ML code development, particularly concerning Large Language Models (LLMs), highlighted the distinct advantages and challenges of both programming languages. The analysis revolved around porting PyTorch implementations to GGML/C++ and Burn/Rust, comparing their performance and correctness within a structured experimental framework. The findings indicated that while Rust showed promise by requiring less token usage initially, the differences in performance and correctness eventually leveled out when factoring in necessary optimizations and a common C API. This investigation is significant for the AI/ML community as it underscores the complexity of choosing a programming language for high-performance tasks, suggesting that both Rust and C++ ultimately present similar computational and efficiency characteristics when utilized effectively. Despite Rust's built-in safety features, the study revealed that neither language fully leveraged their respective advantages, leading to comparable results in terms of bugs and performance. This analysis serves as a reminder of the importance of understanding language features and how they are applied in practice, especially when integrating tools like LLMs to assist with coding tasks.