Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the creation of proprietary intellectual property. A fine-tuned model encodes an organization’s unique intelligence and best practices into its architecture. This builds a competitive advantage that is difficult to replicate with off-the-shelf public frontier models. At the same time, fine-tuning smaller, open-weight models on targeted tasks often matches or exceeds the performance of much larger proprietary models. This approach delivers significant cost savings while keeping sensitive data within secure, private infrastructure.

Amazon SageMaker AI offers a wide selection of open source models and fine-tuning techniques to help organizations tailor foundation models to their unique needs. Now, SageMaker AI introduces serverless model customization for NVIDIA Nemotron 3 models, starting with Nemotron 3 Nano (30B total parameters, 3B active) and Nemotron 3 Super (120B total parameters, 12B active). With supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and reinforcement learning with AI feedback (RLAIF), you can adapt these high-performance open-weight models to your specific domains and workflows without provisioning or managing any infrastructure. For a complete list of open models available for serverless model customization, see Customize open weight models in the Amazon SageMaker AI documentation.