Cloudera Solves the Enterprise AI Bottleneck: Bringing Accelerated AI Directly to Enterprise Data Anywhere

Cloudera is helping enterprises address the pressure agentic AI puts on the infrastructure behind it. Even a single business task can take an agent through multiple systems and interactions before it reaches an answer or takes action.

Those interactions depend on trusted enterprise context, with structured data providing the factual foundation for business. Yet for global enterprises, that ground truth is distributed across public clouds, private data centers, sovereign environments, and the edge.

Accessing data across those environments can quickly become an infrastructure bottleneck for AI. Cloudera research found that 84% of organizations have experienced increased infrastructure costs from AI workloads, while 75% say AI integrations have stalled.

This flips the conventional approach by bringing accelerated AI to enterprise data rather than moving the data to AI, reducing the infrastructure demands created by that movement.

Bring Accelerated Compute to Enterprise Data

Cloudera and NVIDIA approach the infrastructure problem from that direction. Cloudera Anywhere Cloud provides a consistent data and AI foundation across hybrid environments, while NVIDIA Agent Knowledge Foundation provides open models and libraries that help agents retrieve the right evidence, apply consistent business meaning, predict outcomes, and optimize actions using both structured and unstructured data.

Data preparation can become a bottleneck long before a model ever receives a prompt. Native integration of NVIDIA cuDF library into Cloudera Data Engineering brings GPU acceleration to Apache Spark 4.1 workloads while allowing teams to continue using existing PySpark or SQL code. Cloudera expects up to four times workload acceleration on NVIDIA GPUs compared with traditional CPU infrastructure, reducing the time and compute required to prepare AI-ready data.

The same architecture carries into inference. NVIDIA NIM microservices power the Cloudera AI Inference service, enabling teams to deploy optimized models, including NVIDIA Nemotron open models (and soon, new NVIDIA Kumo open models for working with tabular data), within enterprise-controlled environments. This keeps inference closer to the data and applications it supports.

The NVIDIA Nemotron collection of open models extends that connection to enterprise content. Within that collection,  NVIDIA Nemotron Retrieval models help AI agents find and extract enterprise content relevant to a user’s question and use it to produce better-informed answers.

Integrated with Cloudera AI, they can help agents retrieve relevant context from governed data without requiring sensitive information to leave established control boundaries. Taken together, the architecture shortens the distance between enterprise data and the AI systems that depend on it.

Build for the Full Agentic Workload

Keeping AI close to the data solves only part of the infrastructure equation, because agents place different demands on compute than standalone model inference.

A GPU may handle model reasoning, while much of an agent’s work happens around that reasoning as it moves through a task and coordinates activity across enterprise systems. Supporting that ongoing execution at scale makes CPU performance a critical part of the agentic AI stack.

NVIDIA Vera CPU is designed with those agentic workloads in mind, combining NVIDIA Olympus cores, Spatial Multithreading, and high-bandwidth memory built to support latency-sensitive operations and high concurrency. Paired with GPU acceleration, it gives enterprises compute designed for both model reasoning and the execution surrounding it.

“GPUs power the models behind AI agents, but the harnesses that feed those agents context and data rely on heavy CPU-based queries scanning terabytes of enterprise data,” said Sergio Gago, Chief Technology Officer, Cloudera. "Our terabyte-scale Spark testing proves NVIDIA Vera CPU delivers substantial performance gains for enterprise data workloads."

Enterprises can also deploy this optimized CPU/GPU architecture through prevalidated infrastructure, where the underlying hardware and software have already been tested together to ensure they work as an integrated system. Cloudera AI on Dell Technologies Powered by NVIDIA, for example, brings Cloudera’s data and AI platform together with Dell infrastructure and NVIDIA accelerated computing, giving enterprises a way to run private AI alongside their data on premises very quickly and easily, without the need to design, assemble, and configure the entire hardware and software system component by component, avoiding the typical drawbacks and risks associated with do-it-yourself implementations, which are often more science experiments than trustworthy solutions.

The broader implication is that agentic AI performance depends on more than the model itself. The surrounding software, like the AI harness, as well as the hardware infrastructure increasingly determine how rapidly and effectively enterprises can deploy AI in production.

From AI Infrastructure to Enterprise Impact

Trust in a new AI system matters most when it has to operate in and across the environments enterprises already use. Financial institutions, telecommunications providers, and other data-intensive organizations often need AI to work alongside working systems that are and will remain distributed, by design. Bringing accelerated AI computing solutions from Cloudera and NVIDIA into those environments can reduce unnecessary data movement while giving organizations greater control over where AI workloads run.

This is where the value of a hybrid AI architecture becomes more concrete. More of the work required to power AI can happen closer to the enterprise systems and the data that AI depends on.

As agents take on more responsibility across enterprise processes, that proximity will matter even more. Each new workflow increases the interaction between AI and the data estate beneath it. Designing those pieces to work together can remove one of the most persistent barriers between promising AI and AI that can operate at enterprise scale.

Learn how Cloudera and NVIDIA are helping enterprises build and scale AI across hybrid environments, and experience the joint architecture firsthand at upcoming EVOLVE26 events around the world. Find an EVOLVE26 event near you.