Tutel is a high-performance PyTorch library for training and serving Mixture-of-Experts (MoE) models, developed by Microsoft. It provides a drop-in `moe_layer` that integrates with existing models, supporting dynamic parallelism (expert, data, model), capacity scaling, top-k gating, and mixed-precision (FP8, FP4). The library includes custom CUDA/ROCm kernels for MoE-specific operations (dispatch, sparse matmul, quantization) and a JIT compilation system for flexibility. Distributed communication primitives (`batch_all_to_all_v`, `zero_gather`) optimize all-to-all transfers in MoE, while checkpoint tools allow expert redistribution across GPU counts. The codebase also features an automatic SPMD partitioning solver and examples for NLP (Modded NanoGPT-MoE) and vision (CIFAR-10). Although its true value lies in production-grade optimizations for large-scale MoE (e.g., DeepSeek-V3, Qwen3), the tight integration with PyTorch and hardware-specific binaries limits pure reusability outside its intended ecosystem.