NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt
NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt
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Several leading financial institutions in Europe have adopted open-source large models to build proprietary intelligent systems.
At the GTC Berlin conference, European financial institutions including Revolut, Starling Bank, Lloyds, and Santander announced that they are building production‑grade AI capabilities based on NVIDIA’s accelerated computing platform and open‑source weight models—such as PRAGMA, the Nemotron series, and the Kumo family—covering core use cases like trading analytics, speech recognition, fraud detection, and data pipeline acceleration, while emphasizing data sovereignty, cost control, and compliant deployment.
NVIDIA releases a portfolio of open-source models across multiple domains, driving the development of sovereign AI in European industries.
NVIDIA announced the expansion of its open-source model family to encompass areas such as language, robotics, bioinformatics, and quantum computing, unveiling the Nemotron 3.5 series, the Kumo structured-data model, the Cosmos world model, the Alpamayo 2 Edge autonomous-driving model, the BioNeMo RFD4-Proteina protein-design model, and the Ising quantum-computing model. All these models are now available to developers and enterprises, with access to weights and tools, supporting localized customization and independent deployment.
NVIDIA releases Kumo Relational: the first foundation model for structured data that outperforms supervised learning without any training.
NVIDIA has launched Kumo Relational, a pre-trained relational foundation model that can perform predictive tasks—such as churn prediction, fraud detection, and demand forecasting—directly on enterprise‑grade relational databases, without the need for task fine-tuning, feature engineering, or table flattening. In 41 benchmark tests spanning 15 databases, it outperformed supervised models like XGBoost across the board, and on the SAP SALT enterprise benchmark, its MRR metric surpassed AutoGluon by 8%. The model supports databases with up to 500 billion rows and enables zero‑code predictive queries via Predictive Query Language (PQL).