Scalable AI Economics: Achieving Secure, Hybrid Intelligence with Cloudera, AMD, and Dell Technologies

Enterprise interest in generative and agentic AI has accelerated dramatically over the past two years. Organizations across industries are exploring how AI agents, intelligent assistants, and automation can improve productivity, streamline operations, and unlock insights from growing volumes of enterprise data. Yet as enthusiasm grows, so do questions around cost, security, and operational complexity.

One reality is becoming increasingly clear: not every AI workload requires graphics processing units (GPUs) or massive foundation models. In fact, many high-value enterprise use cases can be delivered efficiently using central processing units (CPUs) and smaller, task-focused language models, particularly when deployed close to the data they serve.

A growing number of organizations are now reevaluating their AI strategies through this lens. Rather than pursuing scale at any cost, they are prioritizing return on intelligence: the ability to deploy AI solutions securely, economically, and at scale. This shift is shaping how enterprises think about infrastructure, data architecture, and governance as AI moves from experimentation into production.

A Shift in Enterprise AI Economics

Research from Enterprise Strategy Group (now part of Omdia) indicates that approximately 80% of organizations view AI agents as a top or high business priority. These agents promise tangible benefits through automation, faster decision-making, and improved employee and customer experiences. However, many organizations continue to struggle with the cost and operational burden associated with GPU-centric deployments.

GPU infrastructure can introduce significant capital expense, power consumption, and supply-chain constraints. For many real-time inference and knowledge-driven workloads, this approach can be misaligned with business needs. As a result, enterprises are increasingly exploring alternatives that better match compute resources to workload requirements.

This is where CPU-based AI, paired with smaller language models, has emerged as a practical option. Rather than pursuing the largest possible models, organizations are using the assets they already own to address their budget challenges with GPU purchases or access. This is about right-sizing AI architectures that emphasize efficiency, security, and scalability.

Right-Sized AI and the Role of Small Language Models

Small language models (SLMs) are designed to perform specific enterprise tasks such as summarization, question answering, content generation, and code assistance. Typically containing far fewer parameters than large language models, SLMs can run effectively on modern CPUs while delivering strong performance for targeted use cases.

This approach offers several advantages. CPU-based inference reduces infrastructure costs, lowers power consumption, and simplifies deployment. It also enables organizations to run AI workloads within existing data centers or private cloud environments, addressing concerns around data sovereignty and regulatory compliance.

Within this context, Cloudera has positioned its Private AI strategy around enabling enterprises to deploy and operate AI systems entirely within their own controlled environments. By combining an open data lakehouse architecture with integrated governance and MLOps capabilities, Cloudera supports AI development that remains close to enterprise data.

Infrastructure Matters: CPUs and Enterprise Platforms

The effectiveness of CPU-based AI depends heavily on the underlying infrastructure. Advances in modern processors have significantly improved performance-per-dollar for analytics and inference workloads. AMD EPYC™ processors, for example, are designed to deliver high core density, strong memory bandwidth, and built-in security features, making them well suited for AI inference and data-intensive workloads.

When deployed on enterprise-grade systems from Dell Technologies, organizations can scale AI workloads reliably while leveraging validated architectures optimized for data and AI platforms. This combination allows enterprises to modernize AI capabilities without re-architecting their entire infrastructure footprint.

From an operational perspective, this model enables organizations to reuse existing investments, accelerate deployment timelines, and reduce dependency on specialized hardware. Across these scenarios, the emphasis is not on model size, but on efficiency, responsiveness, and trust.

Practical AI Use Cases With CPUs

Many of today’s most valuable AI applications can run efficiently on CPUs without the need for massive models or GPU acceleration. Examples include:

Internal Knowledge Assistants

Enterprises often store critical knowledge across documents, emails, and reports. By applying SLMs to this data, organizations can enable natural-language access to internal information, improving decision-making while keeping sensitive data on premises.

Employee and Agent Assist Chatbots

HR, IT, and customer support teams face recurring questions that can be automated through secure, internal chatbots. CPU-based AI enables always-available assistance without introducing external data exposure.

Content and Documentation Generation

Marketing, compliance, and engineering teams frequently produce repetitive content. AI-assisted generation and summarization can accelerate workflows while maintaining consistency and governance.

Software Development Support

SLM-powered assistants can generate code snippets, tests, and documentation within enterprise firewalls, helping development teams improve productivity without sending intellectual property to public AI services.

Predictive Analytics and Optimization

In manufacturing and operations, CPU-based AI models analyze sensor and operational data to predict failures and optimize performance, reducing downtime and operational costs.

Data Gravity and the Importance of On-Premises AI

Despite widespread cloud adoption, a significant portion of enterprise data remains on premises. Omdia research indicates that many organizations keep between 26% and 75% of their data in local or private environments. This data gravity presents challenges when AI processing requires moving sensitive information to external platforms.

Private AI architectures address this challenge by bringing AI to the data rather than the other way around. By running AI workloads within existing environments, organizations reduce latency, improve performance, and maintain compliance with regulations such as GDPR, HIPAA, and industry-specific mandates.

Cloudera’s approach integrates data ingestion, governance, model management, and serving within a single platform. Combined with CPU-based infrastructure, this enables enterprises to move from pilot projects to production AI more efficiently.

From Pilot to Production: Measuring Outcomes

One of the most significant barriers to AI adoption has been the gap between proof-of-concept and production deployment. CPU-based AI architectures help narrow this gap by reducing cost and operational complexity.

Organizations adopting this approach report several outcomes:

• Lower total cost of ownership for inference-heavy workloads
• Faster deployment cycles by avoiding specialized hardware procurement
• Reduced energy consumption aligned with sustainability goals
• Improved ROI through workload-appropriate compute selection

These benefits reinforce a growing consensus that enterprise AI success depends as much on economics and governance as it does on model performance.

Conclusion: A Practical Path Forward for Enterprise AI

The next phase of enterprise AI will not be defined by the largest models or the most powerful hardware. Instead, it will be shaped by organizations that can deploy AI securely, economically, and at scale, using architectures aligned with real business needs.

By combining Cloudera’s data and governance platform with AMD EPYC processors and Dell Technologies infrastructure, enterprises have a viable path to operationalizing AI within their own environments. This right-sized approach enables organizations to focus on outcomes, not infrastructure complexity, and to unlock AI value where their data already lives.

As enterprises continue to move AI initiatives from experimentation into production, practical, CPU-based Private AI architectures are likely to play an increasingly important role.

To learn more about achieving economical AI with Cloudera, AMD, and Dell Technologies, download the Omdia Showcase Brief.