Show HN: A 6.9B Moe LLM in Rust, Go, and Python (github.com)

A new 6.9 billion parameter Mixture of Experts (MoE) transformer model has been released, built entirely from scratch using Rust, Go, and Python, incorporating CUDA for performance optimization. This model operates with 1.8 billion active parameters and supports a vast context length, featuring 30 layers and advanced attention mechanisms. The architecture relies on a specialized multi-query attention strategy and utilizes a top-4 expert activation scheme, making it a significant advancement for efficiency in training and inference. The significance of this development lies in its cross-language implementation, showcasing the feasibility and performance of machine learning models across Rust, Go, and Python. The project emphasizes a shared CUDA kernel framework and robust tensor operation capabilities. Benchmarks indicate that the Rust implementation yields superior performance when optimized, while the Go and Python versions also maintain competitive speeds with native operations. This model not only serves as a powerful tool for various applications but also highlights the increasing trend of multi-language collaboration in AI/ML development, enhancing both accessibility and performance across platforms.