Amazon has significantly expanded its SageMaker JumpStart machine learning hub throughout the first three quarters of 2026, adding more than 100 new foundation models (FMs) from 30 distinct providers. The initiative reflects a broader push to accommodate increasing demand for diverse model architectures, sizes, and operational capabilities in enterprise environments.
The updated catalog now includes specialized contributions from major industry players. Alibaba remains the largest contributor with 29 models, including Qwen-based solutions for reasoning, code generation, and agentic workflows. NVIDIA has contributed 17 models, ranging from the Cosmos3 world foundation models for physical AI applications to the Nemotron series for high-performance inference. Other notable additions include Google’s Gemma 4 variants, Mistral’s Ministral family, and new models from IBM and Cohere focused on speech recognition and retrieval-augmented generation (RAG).
Beyond model volume, Amazon’s development efforts have focused on closing the multimodal gap. New additions include models capable of native vision, speech, and document understanding, such as the NVIDIA Nemotron 3 Nano Omni and IBM’s Granite speech-focused models. The company also integrated creative AI capabilities, adding text-to-image and video generation models like FLUX.2 and Meta’s Muse-Glimmer.
To address production requirements, Amazon introduced several efficiency-focused features. These include the addition of quantized and compact models—such as Microsoft’s Phi-4-mini-instruct and JetBrains’ Mellum2-12B—designed to optimize total cost of ownership and latency on smaller infrastructure instances. Additionally, purpose-built models for tasks like document extraction (OCR) and protein structure prediction (OpenFold3) have been added to provide users with specialized alternatives to general-purpose large language models.
Operational enhancements accompanied the catalog expansion to reduce friction in the model lifecycle. A redesigned Model Hub on the SageMaker AI console now allows users to browse and filter models by modality, size, and publisher directly from the landing page. Furthermore, Amazon introduced pre-configured deployment options—optimized for balanced, throughput-heavy, latency-sensitive, or cost-conscious workloads—to eliminate manual benchmarking. A new deep-link integration with Hugging Face also allows developers to navigate directly to pre-configured environments within SageMaker Studio, bypassing manual container and permission setups.
These updates collectively aim to simplify the transition from model discovery to production deployment, offering a broader, more flexible toolkit for enterprises building on Amazon Web Services.
Source: aws.amazon.com
Original article: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-jumpstart-2026-year-to-date-in-review/
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