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granite-speech-4.1-2b, kanana-2-30b-a3b-instruct, and OpenFold3 models now available on Amazon SageMaker JumpStart

BM’s granite-speech-4.1-2b, Kakao’s kanana-2-30b-a3b-instruct, and the OpenFold Consortium’s OpenFold3 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning multilingual speech recognition, bilingual agentic AI, and biomolecular structure prediction, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
granite-speech-4.1-2b is purpose-built for multilingual automatic speech recognition (ASR) and bidirectional speech translation (AST) across English, French, German, Spanish, Portuguese, and Japanese. This compact 2B-parameter speech-language model delivers a word error rate of 5.33% with a real-time factor of ~231, making it one of the most efficient ASR models in its class. Released under Apache 2.0, it integrates seamlessly into enterprise voice workflows for transcription, translation, and audio processing at scale.
kanana-2-30b-a3b-instruct excels in bilingual Korean-English instruction following and agentic AI workflows. Developed by Kakao, it adopts a cutting-edge architecture featuring Multi-head Latent Attention (MLA) and Mixture-of-Experts (MoE), activating only 3B of its 30B total parameters per forward pass for superior throughput. Post-trained with supervised fine-tuning and reinforcement learning, it supports up to 128K tokens via YaRN scaling and is designed to function as an AI collaborator that understands context and acts proactively.
OpenFold3 provides all-atom biomolecular complex structure prediction for proteins, DNA, RNA, and small-molecule ligands. Developed by the OpenFold Consortium and the AlQuraishi Lab at Columbia University, this diffusion-based model extends structure prediction beyond single proteins to model multi-chain complexes and heterogeneous biomolecular interactions. It supports computer-aided drug design and is applicable across academic and pharmaceutical research labs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart

Google DeepMind’s Gemma-4-31B-it-assistant and NVIDIA’s Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring the flagship Gemma 4 31B dense architecture to enterprise workloads in both full-precision and optimized quantized variants, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Gemma-4-31B-it-assistant is built for multimodal reasoning, coding, and agentic workflows as the assistant-tuned variant of Google’s flagship 31B dense model. It handles text and image inputs (including video as frame sequences) and generates text output, with a 256K-token context window and support for over 140 languages. Ranked #3 among open models on the Arena AI text leaderboard—outcompeting models 20x its size—it features a hybrid attention mechanism interleaving local sliding-window and full global attention with native function calling for building autonomous agents.
Gemma-4-31B-IT-NVFP4 delivers the same Gemma 4 31B capabilities at a fraction of the memory footprint. Quantized with NVIDIA’s ModelOpt framework to 4-bit FP4 precision, it reduces memory usage to ~18.5 GB (68% smaller than the base model) and achieves approximately 2.5x faster inference while retaining 97–99% of the original model’s quality. Ideal for cost-efficient, high-throughput production deployments on NVIDIA RTX, DGX Spark, and data center GPUs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models now available on Amazon SageMaker JumpStart

Mistral AI’s Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models from the Ministral 3 family bring compact, vision-capable language models purpose-built for edge deployment and resource-constrained environments, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Ministral-3-3B-Instruct-2512 is engineered for ultra-lightweight edge deployment with multimodal understanding. Comprising a 3.4B language model and a 0.4B vision encoder, it fits in just 8GB of VRAM in FP8 while supporting a 256K-token context window. It offers vision analysis, multilingual instruction following across dozens of languages (including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic), strong system-prompt adherence, and native function calling with structured JSON output—all under the Apache 2.0 license.
Ministral-3-8B-Instruct-2512 delivers frontier-class capabilities comparable to its larger Mistral Small 3.2 24B counterpart in a compact 8B form factor. Built with an 8.4B language model and a 0.4B vision encoder, it fits in 12GB of VRAM in FP8 and features an interleaved sliding-window attention pattern for faster, memory-efficient inference. It shares the same vision, multilingual, agentic, and function-calling capabilities as its 3B sibling while offering stronger reasoning and generation performance.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Qwen3.6-35B-A3B-NVFP4 and Wan2.1-T2V-1.3B-Diffusers models now available on Amazon SageMaker JumpStart

NVIDIA’s Qwen3.6-35B-A3B-NVFP4 and Alibaba’s Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning agentic coding with long-context reasoning and lightweight text-to-video generation, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3.6-35B-A3B-NVFP4 is optimized for agentic coding, multimodal reasoning, and long-context understanding as the NVIDIA-quantized variant of Alibaba’s Qwen3.6-35B-A3B. This Mixture-of-Experts model contains 35B total parameters with only 3B activated per token (8 of 256 experts), supporting a 262K-token context window extendable to ~1M via YaRN scaling. Quantized to NVFP4 using NVIDIA’s ModelOpt framework, it preserves thinking across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines—all at a significantly reduced memory footprint.
Wan2.1-T2V-1.3B-Diffusers excels in text-to-video generation on consumer-grade hardware. Built on the diffusion transformer paradigm with a novel Video Variational Autoencoder (VAE), this 1.3B-parameter model generates high-quality, physics-consistent video clips from text prompts while requiring only 8.19 GB of VRAM. It can produce a 5-second 480p video on an RTX 4090 in approximately 4 minutes, making it one of the most accessible open-source video generation models available.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com