Amazon SageMaker JumpStart: 2026 year-to-date in review

Amazon SageMaker JumpStart is a machine learning (ML) hub within Amazon SageMaker AI that provides access to a curated catalog of AI models, solution templates, and example notebooks. It reduces the time it takes to go from model selection to deployment. You can use it to evaluate open source large language models (LLMs), deploy a vision model for document understanding, or fine-tune a compact model on your own data.

You can access SageMaker JumpStart through the SageMaker Studio console (Models → JumpStart models), programmatically through the SageMaker Python SDK, or directly from Hugging Face using the recently launched deep link integration.

From January through September 2026, SageMaker JumpStart onboarded over 100 new models across 30 distinct providers. This represents a significant expansion of the catalog, driven by the accelerating pace of open source model releases and growing customer demand for choice across model architectures, sizes, and capabilities.

NVIDIA is one of our significant model providers, and in 2026 their contribution extended well beyond language models. The Cosmos3 world foundation models, available in Super, Edge, and Nano tiers, bring video generation, physics simulation, and action planning into a single architecture for physical AI applications such as robotics, autonomous systems, and digital twins. Combined with Nemotron models spanning from efficient edge inference (Nano-12B) to large-scale reasoning (Super-120B), SageMaker JumpStart now offers a broad portfolio of NVIDIA open models. If you build on NVIDIA hardware, you can deploy these models on SageMaker with optimized containers and pre-configured endpoints.

Not every workload requires a frontier-scale model, and many customers are focused on optimizing inference costs as they move to production. We onboarded multiple quantized and compact models: Zhipu AI GLM-5.2 FP8 delivers strong performance with a reduced memory footprint. JetBrains Mellum2-12B uses a mixture-of-experts architecture with only 2.5B active parameters for code generation. OpenBMB MiniCPM5-1B packs multimodal capabilities into 1 billion parameters. Microsoft Phi-4-mini-instruct and IBM Granite 4.0 h-tiny demonstrate that compact models serve production workloads on instance types like ml.g5.xlarge. These models can optimize latency and total cost of ownership (TCO) for targeted use cases.

The first three quarters of 2026 have been a period of significant progress for Amazon SageMaker JumpStart. With over 100 new models onboarded from 30 providers, the catalog now spans a wide range of capabilities. These include frontier reasoning models, multimodal vision-language models, creative AI for image and video generation, speech recognition and synthesis, optical character recognition (OCR) and document understanding, embedding and retrieval models for RAG, agentic workflow models, and specialized scientific computing models.

Reducing concentration risk in model selection is important for enterprise customers. In 2026, we expanded the catalog to include 30 providers, up from a more concentrated set in prior years. Alibaba Qwen is the single largest contributor with 29 models spanning reasoning, coding, speech, and retrieval. Zhipu AI and DeepSeek add competitive LLMs and specialized OCR. MiniMax and Moonshot AI contribute emerging architectures. Cohere brings reliable reranking. BharatGenAI and Kakao expand regional model coverage. This breadth gives customers more options for performance, cost, licensing, and language support.

Previously, discovering SageMaker JumpStart models required navigating into SageMaker Studio and finding the SageMaker JumpStart section in the left navigation. The new Model Hub experience surfaces the full SageMaker JumpStart catalog directly on the SageMaker AI console landing page. You can browse models by category and filter by input/output modality, publisher, model size, license, and framework. You can also view sample notebooks and open models inside Studio with a single click. This avoids the multi-step navigation that slowed initial exploration.

Here’s a breakdown of the leading contributors and a link to our SageMaker JumpStart page :

The models onboarded in 2026 reflect several focused priorities in how we expand the SageMaker JumpStart catalog. These aren’t only industry trends. They represent the areas where we see strong customer demand and significant opportunity to help customers build production AI applications on SageMaker.

Customer workloads increasingly require models that process more than text. In 2026, we prioritized onboarding models that handle vision, speech, and document understanding natively. NVIDIA Nemotron 3 Nano Omni processes text, vision, and speech in a single model. Google Gemma 4 adds vision-language capabilities across multiple sizes. IBM granite-speech-4.1-2b handles speech recognition and translation across six languages. Mistral Voxtral-Mini supports real-time speech understanding. These additions mean customers can avoid chaining separate preprocessing models for multimodal workloads.