NVIDIA and Palantir announced a collaboration that combined NVIDIA’s accelerated computing and foundation models with Palantir’s data and decision platforms, creating an integrated stack for operational AI in enterprises and government.
Executives framed the offering as a first-of-its-kind setup designed to move AI from experiments to real production systems at scale.
The partnership was unveiled at NVIDIA’s GTC event in Washington, D.C., where both companies highlighted an early adopter in large-scale retail and described reference designs for sensitive public sector deployments.
The reveal positioned the stack as a bridge between data integration, optimization, and generative reasoning, with an emphasis on measurable outcomes and governance.
What does the partnership actually deliver
The stack brought Palantir’s Ontology and AI Platform together with NVIDIA’s CUDA X libraries, inference runtimes, and Nemotron reasoning models, so customers could build domain-specific agents that act on real operational data.
The companies described a unified pathway from data ingestion to simulation, planning, and execution, packaged with controls for security and auditability.
Leaders highlighted a common runtime for optimization and generative workflows, which allows teams to simulate scenarios, generate options, and commit decisions with automated guardrails.
The aim was to reduce the gap between insights and action by placing AI directly in planning and logistics systems, rather than leaving models isolated from day-to-day operations.
Did you know?
Retail supply chains often involve tens of millions of daily routing and inventory decisions, which makes combinatorial optimization a prime candidate for GPU acceleration in real time settings.
How does the Lowe’s pilot demonstrate value?
A flagship pilot at Lowe’s created a live digital twin of its supply network, spanning thousands of vendors, hundreds of distribution centers, and more than a thousand stores.
The environment is continuously updated, allowing planners to test scenarios that respond to weather, transport constraints, and demand spikes, and then propagate changes into execution systems.
The pilot emphasized dynamic replanning in minutes, not days, using GPU-accelerated optimization to rebalance routes and capacity and to select feasible alternatives under variable constraints.
Leaders said the approach helped align shelf availability, delivery times, and cost targets, while improving resilience to disruption across the chain.
What technology pieces power the stack
Core components included NVIDIA CUDA X data science libraries for accelerated data pipelines, Nemotron models for reasoning and tool use, and GPU infrastructure for training and inference.
Palantir contributed Ontology for semantic data integration and governance, plus the AI Platform for agent orchestration, simulation, and policy controls that tie into existing enterprise systems.
NVIDIA cuOpt handled decision optimization for routing and capacity allocation in time-sensitive scenarios.
The roadmap called for Blackwell architecture support to expand performance headroom, enabling larger models and faster end-to-end workflows.
Together, these elements aimed to shrink compute costs per decision and increase responsiveness for operations teams.
How will the government and regulated sectors use it?
The companies presented an AI Factory for Government reference design geared to agencies that require data privacy, supply chain security, and transparent audit trails.
The architecture combines on-premises or sovereign-cloud options with reproducible pipelines, enabling mission systems to adopt AI while meeting strict compliance obligations.
Use cases spanned defense logistics, emergency response, and public health operations, where decision speed and accountability matter.
By integrating data lineages, access controls, and policy enforcement, the stack sought to allow mission owners to trace recommendations, verify model behavior, and adopt AI under established risk management frameworks.
What near-term ROI can enterprises expect?
Enterprises could see gains in network-wide optimization, inventory positioning, and service-level recovery during disruptions, thanks to faster simulation cycles and automated plan generation.
Benefits accrue when AI agents connect scenario planning to the transaction layer, reducing manual handoffs and improving time-to-resolution.
Cost impacts come from lower compute per scenario, reduced lost sales during shocks, and leaner inventories through continuous rebalancing.
Early adopters may also improve workforce productivity by using agent copilots that translate strategic objectives into executable plans, while retaining oversight through policy and approval workflows.
The collaboration placed decision intelligence at the center of AI transformation, linking GPUs, reasoning models, and governed data into one operational fabric.
If the reference designs translate across industries, integrated stacks could compress deployment timelines and normalize AI in mission-critical workflows, setting a practical template for scale.
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