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.
SageMaker JumpStart serves three primary use cases:
- Model discovery and evaluation — Browse hundreds of foundation models (FMs) from providers like Alibaba (Qwen), NVIDIA, Google, Mistral, Cohere, IBM, and more. Each model card includes architecture details, benchmarks, licensing information, and sample notebooks.
- One-click deployment — Deploy your choice of model to a SageMaker real-time inference endpoint with pre-configured settings optimized for your workload (low cost, low latency, throughput, or balanced). No manual container or instance configuration required.
- Model customization — Fine-tune models directly from the console or SDK using techniques like low-rank adaptation (LoRA), supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning with verifiable rewards (RLVR). Serverless customization options reduce infrastructure management.
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.
2026 onboarding at a glance
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.
Here’s a breakdown of the leading contributors and a link to our SageMaker JumpStart page:
- Alibaba (Qwen): 29 models spanning reasoning (Qwen 3.8-27B), large-scale mixture-of-experts (Qwen3.5-122B-A10B), agentic workflows (Qwen-AgentWorld-35B-A3B), code generation (Qwen3-Coder-Next, Qwen 3 Coder 30B), speech recognition (Qwen3-ASR-1.7B), text-to-speech (Qwen3-TTS), embeddings (Qwen3-VL-Embedding-2B), and reranking (Qwen3-Reranker-4B).
- NVIDIA: 17 models including the Cosmos3 world foundation model family (Super, Edge, Nano), Cosmos Reason 7B, MSA-Search, Nemotron 3.5 Lightning, Nemotron-3-Super-120B (FP8 and BF16), Nemotron3 Ultra, Nemotron-Nano-12B-v2, Nemotron 3 Nano Omni (multimodal), and Nemotron 3.5 Super VL for vision-language.
- Mistral: 7 models including the Ministral-3 family (3B, 8B, 14B in standard and BF16) and Voxtral-Mini-4B-Realtime for speech.
- Google: 6 Gemma 4 variants, including gemma-4-31B-it, E2B-it, E4B-it, 26B-A4B-it, assistant, and NVFP4 quantized.
- IBM: 4 models including Granite 4.0 h-micro/small/tiny and granite-speech-4.1-2b for multilingual automatic speech recognition (ASR) across 6 languages.
- Cohere: 4 models including Command A (A100) and Rerank v3.5, v4.0 Fast, and v4.0 Pro for retrieval augmentation.
- Others: 33 models from MiniMax, Zhipu AI (GLM), DeepSeek, Moonshot AI, Allen AI, Microsoft, Meta (Muse Spark), Reflection (Beam), Black Forest Labs, JetBrains, OpenBMB, BharatGenAI, Kakao, OpenFold, and more.
Model onboarding highlights
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.
Expanding the NVIDIA open-model portfolio
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.
Closing the multimodal gap
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.
Making creative AI accessible
Image and video generation moved from niche to mainstream in 2026. We onboarded FLUX.2 Small Decoder and FLUX.2-klein-base-4B from Black Forest Labs for fast text-to-image generation, Meta Muse-Glimmer-30B for multimodal agentic workflows, and NVIDIA Cosmos3 for video generation. With these models, you can access creative AI capabilities with the same one-click deployment experience you use for language models.
Prioritizing efficient and quantized models
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.
Onboarding purpose-built models for targeted tasks
General-purpose LLMs are versatile, but purpose-built models are optimized for specific tasks. In 2026, we onboarded Qwen-AgentWorld-35B-A3B for autonomous AI workflows with native tool-calling. GLM-OCR, LightOnOCR, and DeepSeek-OCR handle document text extraction. Qwen3-Reranker-4B and Cohere Rerank v4.0 improve retrieval precision in Retrieval Augmented Generation (RAG) pipelines. langcache-embed-v3-small provides lightweight embedding for semantic search. Qwen3-ASR and Qwen3-TTS bring speech recognition and synthesis. OpenFold3 supports protein structure prediction for scientific computing. With these models, you can build pipelines where each stage uses the specialized tool for the job, rather than forcing a single model to do everything.
Broadening provider diversity
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.
SageMaker JumpStart enhancements in 2026
Beyond model onboarding, SageMaker JumpStart also shipped significant improvements in 2026 that reduce friction across the model lifecycle, from discovery to deployment.
Simplified Model Hub on the SageMaker AI console
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.
Pre-configured endpoint deployment options
Choosing the right instance type, serving container, and optimization settings for a model deployment typically required manual benchmarking across dozens of combinations. SageMaker JumpStart now offers named deployment configurations, with optimization options for:
- Balanced — An even tradeoff between cost, latency, and throughput for general-purpose workloads.
- Throughput optimized — Maximum tokens per second using techniques like Speculative Decoding, ideal for high-volume batch processing.
- Latency optimized — Lowest Time to First Token (TTFT), prioritizing speed for interactive applications.
- Cost optimized — Lowest infrastructure cost per request, suitable for workloads that can accept higher latency.
These configurations include Neuron-optimized setups for AWS Inferentia and AWS Trainium instances, reducing deployment complexity and infrastructure costs. You can deploy a model with a single click and the right optimization profile, rather than spending weeks benchmarking configurations manually.
Hugging Face deep link integration
A new integration with Hugging Face connects model discovery directly to SageMaker Studio, for onboarded models. When browsing models on Hugging Face, you can choose Deploy on SageMaker AI or Customize and Deploy on SageMaker AI. You land directly in a configured Studio environment with the model pre-loaded, permissions pre-configured, and GPU quota visibility built in. This avoids the need to set up AWS Identity and Access Management (IAM) roles, create a domain, or configure permissions before experimenting.
Getting started
There are three ways to get started with SageMaker JumpStart:
- SageMaker Studio console — Open SageMaker Studio, navigate to Models → JumpStart models, and browse the catalog. Choose a model to view its details, then choose Deploy or Customize.
- SageMaker Python SDK — Deploy models programmatically using the SageMaker Python SDK. Visit the SageMaker JumpStart model catalog for SDK examples and deployment guides.
- Hugging Face deep link — Browse the HuggingFace Model Hub and choose Deploy on SageMaker AI to land directly in Studio for SageMaker JumpStart onboarded models.
Each model on SageMaker JumpStart includes deployment configurations, sample notebooks, and documentation. For a hands-on walkthrough, see our related blog post on “Deploy and evaluate models with Amazon SageMaker JumpStart”.
Conclusion
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.
These onboarding priorities reflect where we see the strongest customer demand and the greatest opportunity to accelerate AI adoption on AWS. They include expanding the portfolio, closing the multimodal gap, making creative AI accessible, prioritizing efficiency and quantization, onboarding purpose-built models, and broadening provider diversity.
Combined with the enhancements like simplified Model Hub, pre-configured deployment options, and the Hugging Face deep link integration, SageMaker JumpStart continues to lower the barrier between model discovery and production deployment. Visit the SageMaker JumpStart model catalog to explore the full lineup.
Next steps
There are many ways to get started with SageMaker JumpStart. Try one of these out for yourself!
- Evaluate models and choose the right one for you
- Learn how to Deploy from HuggingFace to SageMaker Studio
- Use pretrained models in case-based deployments
- Design a Physical AI model factory on SageMaker HyperPod