sankalphegde/Sigmoid-TopK-Fusion

Sigmoid-TopK-Fusion is a niche performance optimization targeting a specific bottleneck in Mixture-of-Experts (MoE) architectures. While the claimed 3.1x speedup over PyTorch baselines is significant, the project currently functions as a code snippet or micro-library rather than a defensible product. With 0 stars and forks and being only 8 days old, it lacks any community adoption or ecosystem. The defensibility is low (2) because the logic is a straightforward application of Triton to a known mathematical sequence; any engineer at a frontier lab (OpenAI, DeepSeek) or an inference framework team (vLLM, TensorRT-LLM) could replicate or surpass this fusion in a few days. Frontier risk is high because MoE routing is a core area of focus for hardware-aware optimization; frameworks like PyTorch Inductor or dedicated engines like vLLM are increasingly automating these fusions. This project is highly likely to be displaced within 6 months as standard inference engines incorporate similar optimizations natively.