NVIDIA and Palantir Use Open AI Models to Optimise Supply Chain Decisions from Wafer to First Token

NVIDIA has published a post and accompanying video setting out how it uses its own AI tools, combined with Palantir’s data platforms, to manage one of the most complex supply chains in the technology industry — the journey of components from semiconductor fabrication through to the moment an AI workload produces its first output on deployed hardware.

The post, framed around the phrase “from wafer to first token, every step matters,” describes a working system rather than a research concept. It features Alex Neefus, Senior Director of Solution Architecture at NVIDIA, who also presented the approach at AIPCon 11, a Palantir-associated conference focused on operational AI deployments.

What the System Actually Does

At its core, the collaboration links NVIDIA’s Nemotron open-weight large language models with Palantir Foundry, Palantir’s Artificial Intelligence Platform (AIP), and the Palantir Ontology — a structured representation of an organisation’s operational data and the relationships between entities within it. Together, these tools are intended to give supply chain planners end-to-end visibility of constrained components, such as advanced semiconductor wafers and high-end GPUs, and to support faster allocation decisions across data centre customers and internal needs.

NVIDIA’s cuOpt software, a GPU-accelerated optimisation tool, runs inside Palantir AIP to handle scenario planning. According to NVIDIA’s own technical reporting, the system can evaluate millions of planning scenarios in real time, compared with the handful that planners could reasonably assess manually. That figure is a company capability claim and has not been independently verified.

The model at the centre of this is not a large, general-purpose system. NVIDIA post-trains Nemotron 3.5 Lightning — a smaller, purpose-built variant — on its own supply chain data and on feedback from expert planners. The result, according to NVIDIA, is a model that can recommend actions, explain trade-offs between competing allocation options, and flag emerging risks, while human planners retain final decision authority. NVIDIA also reports that Nemotron 3.5 Lightning can outperform the much larger Nemotron 3 Ultra on specific supply chain tasks, and that retraining can be done on a single GPU in a matter of hours — though again, this is the company’s own figure.

The “Sovereign AI Stack” Framing

One aspect of the collaboration that NVIDIA and Palantir emphasise is data control. Both companies describe the approach as a “sovereign AI stack” — meaning that organisations deploying it keep their proprietary operational data within their own environment rather than sending it to an external model provider. Open-weight models like those in the Nemotron family can be deployed and fine-tuned on-premises or within a controlled cloud environment, which is chiefly relevant for sectors with strict data governance requirements.

Alex Neefus, Senior Director of Solution Architecture at NVIDIA, said: “From wafer to first token, every step matters.”

That framing speaks to a broader point about the complexity of NVIDIA’s own supply chain. The company describes it as running from the wafer stage at semiconductor fabs, through systems assembly, and on to the moment deployed AI hardware processes a live workload. Managing allocation across that chain, under conditions of constrained supply and high customer demand, is the practical problem the system is designed to address.

NVIDIA has also introduced an internal metric called Time of Ownership (TOO) to measure how quickly components move through its supply chain and into production. It is a proprietary measure, not an external industry standard, but it reflects an attempt to quantify the cost of delay at each stage.

Human Oversight and the Limits of Automation

The emphasis on keeping human planners in control is deliberate. Both NVIDIA and Palantir have been careful, in their public presentations, to position the AI as a decision-support tool rather than an autonomous decision-maker. Planners use the system to understand trade-offs and model constraints; they don’t hand authority to it.

But critics of large-scale AI in supply chains raise questions that the companies’ presentations don’t fully address. If the training data or scenario assumptions are flawed, the recommendations the model generates could be systematically wrong in ways that aren’t immediately visible. Palantir’s platforms, which integrate detailed operational data across organisations and sometimes across borders, also attract scrutiny over data privacy — especially when similar tools are considered for public services.

Labour advocates have raised separate concerns about AI-optimised logistics, specifically the pressure placed on workers in warehouses and distribution centres to match schedules set by systems they have no visibility into. Neither NVIDIA nor Palantir addressed those questions directly in the materials published around AIPCon 11.

What Comes Next

NVIDIA describes the training process as an “AI flywheel” — a loop in which expert planners’ decision-making is codified, synthetic data is generated to cover rare disruption scenarios, and models are continuously updated as new data arrives. The intention is that the system improves over time as it processes more real-world planning decisions.

Whether other organisations outside NVIDIA adopt the same stack remains an open question. The Palantir platforms involved are already in use across defence, government, and commercial logistics sectors, so the technical components exist. But the degree to which competitors or partners replicate NVIDIA’s specific approach to post-training on proprietary supply chain data is not yet clear from public information.

What This Means for Kent Residents

For Kent residents and businesses, the practical effects are indirect. Kent’s logistics sector — including distribution centres and operations connected to cross-Channel trade through the county’s ports — could, over time, benefit from more reliable delivery timelines for technology hardware if global supply chain optimisation reduces bottlenecks at the source. More broadly, UK consumers and organisations waiting on AI computing infrastructure, from universities to NHS trusts, may see more predictable access to NVIDIA hardware if the approach demonstrably reduces allocation delays — though that outcome depends on how widely similar methods are adopted across the industry.