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. 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 Ju