Nvidia hopes to capitalize on European sovereign AI demand by unloading a slew of new open weights models spanning chatbots, healthcare, and robotics. The major model devs have the resources to build highly capable models from scratch. But most enterprises don't, and have instead been forced to fine tune open weights models from the likes of Mistral or H Company using their own datasets. At GTC Berlin, Nvidia unveiled its latest additions to its open weights model library with refreshed Nemotron Retriever embedding and Alpamayo autonomous vehicle models.

The point of embedding models While by no means the sexiest subclass of models out there, embedding models are some of the more useful ones for enterprise apps. Retrieval augmented generation (RAG), a technology we've explored extensively in the past, relies heavily on the embeddings generated by these models for applications like AI-augmented search. At a high level, RAG works by cutting up documentation in chunks, then converting those into vectors called embeddings.

The embeddings are then ranked based on their relevance to the user's query before they're fed into an LLM's context.

Nvidia's latest embedding and reranking models, based on Nemotron 3.5, support embeddings of both images and text across 34 languages. This time around, the models are being offered in sizes ranging from one to eight billion parameters, with NVFP4-quantized versions for those running on new Blackwell-based accelerators. That's small enough to run on something like a DGX Spark with plenty of room to spare for other models. Always take vendor claims with the usual grain of salt, but among competing embedding models, Nvidia boasts that at BF16-precision, its new 8 billion parameter embedding and reranking models are now the top-ranked dense models on the ViDoRe V3.1 benchmarks. However, not all documents are easily converted into embeddings. PDFs are notoriously difficult for embedding models to make sense of.

They're often scanned from printed source material and formatted for consumption, which means scraping text and parsing charts. For this, Nvidia has also launched a refreshed Nemotron Parse model that's been trained specifically to extract information from PDF documents. Nvidia claims the model can process roughly 10.5 pages a second when running the one billion-parameter model on a single H100.

Alongside the new embedding models, Nvidia is also rolling out access to Alpamayo 2 Edge, a more compact version of its autonomous driving model based on which promises real-time processing of streaming video and sensor data when running on the company's DRIVE AGX Thor platform.

The models come just a week after the GPU giant launched its Nemotron 3.5 Super vision language (VL) model and teased Nemotron 3.5 Bolt, a 74 billion-parameter mixture of experts (MoE) model designed to fit in as little as 24 GB of video memory. That model, Nvidia says, will be available for download starting Friday.

While open models provide enterprises and governments a path toward sovereign AI, Nvidia's models are somewhat unique in how they're released. Most open weights models are a sort of black box. Anyone can download them, but the data and processes used to arrive at those weights is a mystery. This isn't the case with many of Nvidia's models: Alongside their weights, Nvidia routinely releases the training data and the recipes used to turn it into the final model. This means customers can not only inspect the model at a low level, but build a fully custom model based on Nvidia's Nemotron architecture, rather than trying to modify the weights after the fact through fine tuning.

We should note that Nvidia is not the only one to do this. The Allen Institute has employed a similar strategy with its OLMo models for this very reason. ®