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Excited to announce our collaboration with NVIDIA and Nebius at NVIDIA GTC Berlin!! We post-trained NVIDIAs Nemotron 3.5 Lightning into a specialized model that outperforms Anthropic Claude Haiku 5.5 at 44× lower cost and matches OpenAIs GPT-6 Luna at 40× lower cost on a real-world enterprise task, with Nebius Token Factory providing the infrastructure to scale. With Plurai's vibe-training, any engineer can go from a task description and a few production examples to a post-trained, benchmarked, production-ready model in under 20 minutes. What excites me most is that this same workflow can be applied to any open-source model, turning it into a specialized, frontier-beating model at a fraction of the cost. Huge thanks to our incredible partners at NVIDIA ( Esti Etrog, Amit Bleiweiss, Gilad Gershonr, Arik Kol, Mehran Maghoumi, Anjali Shah, Dr. Sören Petersen) and Nebius ( Joe Sarabia, Devang Sachdev, Tomer Avlakosh, Adrian Klingenberg, Harald Brauer, Adam Lanteigne, David Schulman and Douglas Shapiro) for the close collaboration and for pushing the boundaries of whats possible together. This is just the beginning, with much more to come! Full research: https://lnkd.in/dAURsTvt

New on our blog: our work with NVIDIA and Nebius. / How we turned an open Nemotron model into one that beats Claude Haiku-5.5 by 10 points, at 44x lower cost, on a real enterprise workload. Inside: the full benchmark, cost per 1,000 calls and the step-by-step process.

10 points higher quality than Claude Haiku-5.5. 44x lower cost. / That’s an open NVIDIA Nemotron model, post-trained by Plurai on a real enterprise workload. It also matched GPT-6 Luna at 40x lower cost. No labelled data. No ML team. Every number is on our blog.

Teams keep high-volume tasks on frontier models for one reason: a custom model used to mean labelled data, an ML team and weeks of iteration. We started from a task description instead. Plurai generated the training data, post-trained an open Nemotron model, and benchmarked it. The result beat Claude Haiku-5.5 by 10 points at 44x lower cost.

Most enterprise AI workloads are extraction, classification and routing. High volume, narrow tasks. Most still run on a frontier model, because building a custom one meant labelled data and an ML team. We post-trained an open NVIDIA Nemotron model for a global enterprise. It beat Claude Haiku-5.5 by 10 points at 44x lower cost. The full breakdown, with NVIDIA and Nebius, is on our blog.

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