NVIDIA has launched NeMo AutoModel, an open-source library designed to dramatically accelerate the fine-tuning of large-scale Mixture-of-Experts generative AI models. By integrating directly with HuggingFace Transformers v5, the library delivers significant performance gains while maintaining full API compatibility for the broader developer community. NeMo AutoModel enhances Transformers v5 newly introduced MoE foundations, including expert backends and dynamic weight loading, with three core optimizations: Expert Parallelism, DeepEP fused all-to-all dispatch, and TransformerEngine kernels. These components work in tandem to overlap communication with computation, shard expert weights across GPUs, and accelerate core mathematical operations. The library handles over twenty MoE architectures by leveraging v5 reversible weight conversion, ensuring optimized execution without requiring per-model checkpoint plumbing. In benchmark testing across single-node and multi-node configurations, NeMo AutoModel achieves 3.4 to 3.7 times higher training throughput and reduces peak GPU memory consumption by 29 to 32 percent compared to native Transformers v5. The Expert Parallelism implementation is particularly critical for scaling, enabling the full fine-tuning of NVIDIA 550-billion-parameter Nemotron 3 Ultra model across sixteen nodes. This configuration runs efficiently where standard v5 implementations fail due to memory constraints. Single-node benchmarks on 30-billion-parameter models confirm consistent speedups in forward and backward passes, driven by optimized routing gates and fused expert matrix multiplications. The library prioritizes frictionless adoption. Developers can swap in NeMo AutoModel with a single import statement, requiring zero modifications to existing training scripts. All optimized checkpoints are saved in standard HuggingFace safetensor format, ensuring immediate compatibility with leading inference engines like vLLM and SGLang. Expert Parallelism operates as a dedicated parallelism dimension, allowing developers to combine expert sharding with data parallelism on the same hardware without mesh reconfiguration. NeMo AutoModel represents a strategic bridge between open-source model development and enterprise-scale training efficiency. Configuration files, benchmark scripts, and full source code are publicly available through the NeMo AutoModel repository, offering the AI community a streamlined path to optimize frontier MoE architectures without sacrificing workflow continuity.