NVIDIA used its Berlin keynote on October 21 to expand its open-model portfolio across an unusually wide range of domains — language, retrieval, structured business data, robotics, autonomous driving, biology and quantum computing — framing the release as infrastructure for European sovereign computing.
The announcement is best read as a single argument rather than seven separate product launches. The premise is that the scarce resource in enterprise AI is no longer model capability, but the ability to run models on infrastructure an organisation controls, tuned to its own data and its own industry.
Nemotron and the language layer
The Nemotron family carries the language and vision work, and this cycle added two flagship models released the previous week: Nemotron 3.5 Super VL, an open 120-billion-parameter model positioned for always-on agents operating across text and images, and Nemotron 3.5 Bolt 74B-A9B 24GB, a smaller model sized for edge deployment. A further variant, Nemotron 3.5 Bolt E4B 4GB, is expected on October 23. Super VL is available on Hugging Face, ModelScope and OpenRouter, as a NVIDIA NIM microservice on build.nvidia.com, and through NVIDIA's cloud, post-training and inference partners.
The Nemotron models are built with data, research and expertise from the Nemotron Coalition, whose European members include Black Forest Labs, H-Company and Mistral. Named adopters span Dassault Systèmes, Salesforce, SAP, Shopify, Synopsys, WPP, Palantir's customers, and the professional services firms Accenture and EY. The post-training partner list is longer still, including AfterQuery, AI21, Baseten, CoreWeave, Fireworks, Google Cloud, Mercor, Scale AI and TNG.
Kumo, and the bet on structured data
The most consequential release may be the least discussed. NVIDIA Kumo is a family of open foundation models, libraries and tools for prediction on structured business data — forecasting outcomes, scoring fraud risk, recommending actions, detecting anomalies. It is part of the NVIDIA Agent Knowledge Foundation.
The argument NVIDIA is making is about a known weakness of large language models: most of an enterprise's data sits in tables, and LLMs are not naturally good at querying them. Kumo's pitch is that a pretrained model plus fine-tuning can replace the hand-built machine learning pipeline that today has to be assembled separately for every prediction task. Kumo Structured covers relational and tabular prediction, forecasting and anomaly detection; Kumo Engine is the open library stack for developing and deploying structured-data foundation models.
Cognite, Coinbase, Databricks, DoorDash, Revolut and Tubi are among the first to work with the models, and Snowflake customers are already using them across advertising, energy, financial services and retail. Kumo Forecast, announced separately, is an open multivariate time-series model combining a pretrained MOMENT-1-large transformer backbone with optional retrieval.
Notably, NVIDIA published no performance numbers for Kumo. That absence is itself informative: the case being made is structural, that the pipeline is unnecessary, rather than that a specific model beats a specific baseline.
Retriever, Cosmos, Alpamayo, BioNeMo and Ising
The remaining releases each cover a distinct layer.
Nemotron Retriever extends the collection with five open, commercially licensed models: Nemotron 3.5 Embed 8B and 1B for search across text and document images including tables, charts and infographics, with an NVFP4-quantised version of the 1B; Nemotron 3.5 Rerank 8B for refining result ordering; and Nemotron 3.5 Parse, a 631-million-parameter model that turns document images into structured text. They run as NIM microservices and on Baseten, DeepInfra and FriendliAI.
Cosmos 3.5 is the world-model layer for robotics and driving, and it is where the tooling has matured furthest. New post-training and optimisation tools help improve world models with less compute, alongside simulation checkpoints for evaluating behaviour in interactive driving and robotic manipulation before real-world testing. Cosmos Data Factory adds blueprints and agent skills for curating, labelling, generating and evaluating training data across driving scenarios, product defects, image augmentation and event generation, usable whole or in pieces with agents coordinating. Porsche with Voxel51, ANYbotics with Encord, Zalando, CEVA Logistics with Sereact, and Siemens are using it. NVIDIA says the updated Cosmos 3 NIM delivers up to twice the inference speed with no quality loss in validated configurations, and the family is now open on Hugging Face and GitHub.
Alpamayo 2 Edge is a compact reasoning model for autonomous vehicles, built on Cosmos and optimised for the DRIVE AGX Thor platform, intended to bring real-time scene understanding and motion planning into vehicles with lower compute requirements. It arrives on GitHub and Hugging Face later this month.
BioNeMo RFD4-Proteina, developed with the Baker Lab at the University of Washington's Institute for Protein Design, generates protein sequences and 3D structures for targets that are difficult to bind. NVIDIA reports leading performance across more than ten design tasks and generation roughly thirteen times faster than RFdiffusion3 in a benchmark of 200-amino-acid proteins, with the model available through agents so candidates can be evaluated against design goals before lab work begins. It is reachable through Anthropic's Claude Science, OpenAI, Google Cloud and Amazon Bio Discovery.
Ising is the quantum layer: open models for real-time hardware calibration and error correction across qubit modalities. IQM Quantum Computers is using it to calibrate processors, SEEQC is pairing it with the NVQLink platform and its Digital Link for low-latency error correction, and AWS is integrating the family into AWS Parallel Computing Service so researchers can build on it before deployment.
The sovereign framing
The portfolio's centre of gravity is European, and the framing is deliberate. Sovereign AI, in this usage, means the ability to build and run models on infrastructure under national or organisational control, with the data staying where the operator wants it. The Nemotron Coalition's European membership, the Berlin venue, and the emphasis on open weights and self-hosted deployment form a consistent argument.
There is a commercial logic underneath it as well. Open models expand the addressable market for the hardware and software that runs them. If enterprises train their own agents on their own data, the training and inference requirements stay with NVIDIA's stack rather than migrating to a hosted frontier API. For a company whose customers are governments and regulated enterprises, that is the durable position.
What the announcement does not do is settle the open questions. No performance figures for Kumo, no comparison against existing tabular-modelling approaches, and no indication of what the European sovereign deployments cost in practice. The claim is that the models exist, that they are open, and that the organisations named have already begun work with them. Whether the resulting systems are better than what buyers assemble themselves remains a question for their own data.