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.
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Source: Amazon Web Services
