Oct 13, 2026 · The CTO Advisor · 5 min read

Optimizing Enterprise AI Compute and Data Infrastructure

Rob Lee of Everpure discusses distinguishing compute shortages from data inefficiencies, the shift toward data-co-located AI processing, and the strategic importance of KVCache offloading. The analysis highlights how enterprises can reduce token waste and improve agent accuracy by restructuring data access layers.

Technology Business Strategy

The takeaways

Prioritize Task Latency Over Raw Utilization. Shift KPI focus from GPU utilization percentages to task completion time and accuracy. This ensures compute resources are spent on useful processing rather than inefficient data handling.
Co-locate AI Compute With Transactional Data. Move inference engines closer to transactional data sources to support real-time agent tasks. This reduces latency for fraud detection and security decisions that cannot tolerate analytics-level delays.
Break Silos With Unified Data Access. AI agents require cross-system data integration to make accurate decisions. Enterprises must move beyond single-application analysis to leverage unstructured and structured data in unison.
Implement Multi-Layer Memory Hierarchies. Deploy KVCache offloading to remote storage to prevent redundant GPU computations. This creates a new memory hierarchy that extends beyond HBM and local DRAM for long-context sessions.
Reduce Token Waste Via Data Curation. Feed only relevant data to LLMs to minimize token consumption. Efficient data retrieval reduces compute load and improves the cost-efficiency of enterprise AI deployments.
Differentiate Infrastructure From Data Readiness. Separate strategic investments into raw infrastructure speed and data intelligence. Data readiness ensures the right context is available, while infrastructure ensures it is processed quickly.

The Shift from Raw Compute to Useful Processing

Enterprise leaders are increasingly facing a critical distinction: is a performance bottleneck a genuine compute shortage or a data inefficiency disguised as one? Rob Lee of Everpure argues that raw GPU utilization is a secondary metric. The primary KPIs for AI infrastructure should be task completion time and accuracy. If an agent takes too long to complete a task, it is often due to feeding unoptimized data into the model, resulting in "useless compute." By optimizing for speed and accuracy first, utilization naturally improves, reducing the need for excessive hardware procurement.

Data Gravity and Transactional AI

The traditional model of moving data to analytics warehouses is insufficient for agentic AI. As AI agents begin handling transactional flows such as fraud detection and credit approvals, compute must move closer to the data. This "data gravity" shift requires co-locating inference engines with transactional data sources to meet real-time latency requirements. This approach breaks down historical silos, as AI agents require access to unified data across ERP, CRM, and unstructured sources to make holistic decisions.

The KVCache and Memory Hierarchy Opportunity

A major emerging challenge is the cost of maintaining context in long-running agent sessions. KVCache offloading is becoming a critical infrastructure component, acting as a new tier in the memory hierarchy. By offloading cache to remote storage, enterprises avoid recomputing the same context on expensive GPUs. This strategy is essential for scaling inference environments that serve multiple users simultaneously. Everpure’s strategy differentiates between raw infrastructure speed and data intelligence, emphasizing that data readiness—understanding the meaning and correlation of data—is as vital as raw processing power. Enterprises that master this dual approach will achieve higher ROI from their AI investments by minimizing token waste and maximizing operational efficiency.

Conclusion

The future of enterprise AI lies in the integration of data and compute. Leaders must move beyond hardware-centric metrics to focus on data quality, context management, and cross-system integration to unlock the full potential of agentic systems.

Key insights

Raw GPU utilization is a lagging indicator; task latency and accuracy are leading indicators of infrastructure efficiency. Optimizing for useful compute reduces unnecessary hardware spend.

Operational Efficiency →

Impact: Reduces capital expenditure on hardware by identifying data bottlenecks before scaling compute resources.
Agentic AI requires real-time access to transactional data, necessitating a shift from centralized data lakes to co-located compute environments. This breaks down traditional application silos.

Data Architecture →

Impact: Enables real-time decision-making for fraud detection and security, improving operational risk management.
KVCache offloading is emerging as a critical component of the AI memory hierarchy, preventing redundant GPU computations in long-context sessions. This extends the effective memory capacity of inference systems.

Technology Infrastructure →

Impact: Lowers inference costs and improves scalability for multi-user AI deployments by reducing GPU load.
AI agents require cross-system data integration to function effectively, moving beyond single-application analysis. This creates a demand for unified data access layers across ERP, CRM, and unstructured sources.

Data Strategy →

Impact: Improves decision accuracy by providing holistic context, reducing the risk of isolated or biased AI outputs.
Data readiness is distinct from infrastructure speed; understanding data meaning and correlation is as important as processing power. Efficient data curation minimizes token waste and improves LLM performance.

Data Intelligence →

Impact: Enhances ROI on AI models by ensuring high-quality input, reducing operational costs associated with token consumption.

Action items

Audit current AI workloads to identify tasks with high latency but low accuracy. Implement data optimization protocols to reduce the volume of data fed into LLMs for these specific tasks.

Impact: Improves task completion speed and reduces token costs, directly impacting the bottom line of AI operations.
Evaluate the location of inference engines relative to transactional data sources. Consider co-locating compute resources with critical transactional databases to meet real-time latency requirements.

Impact: Enables real-time agentic capabilities for security and fraud detection, enhancing operational resilience.
Invest in KVCache offloading solutions to extend the memory hierarchy of AI inference systems. This involves integrating remote storage tiers to manage long-context sessions efficiently.

Impact: Reduces GPU utilization costs and improves the scalability of AI services for high-concurrency environments.
Develop a unified data access layer that integrates structured and unstructured data across enterprise systems. This ensures AI agents have access to holistic context for decision-making.

Impact: Breaks down data silos, improving the accuracy and reliability of AI-driven business processes.
Implement data intelligence tools to understand data provenance, meaning, and correlation. This ensures that the right data is retrieved for the right question, minimizing inefficient compute usage.

Impact: Enhances data readiness, leading to more efficient AI model performance and reduced operational waste.

Quotes

“KPI number one I look at is, okay, so for, the agent I'm building that I'm deploying to perform some business process, well, how long is that thing taking?”

“AI now creates both the ability as well as the impetus to look at all of an enterprise's data sources, no matter where they sit, in conjunction with one another.”

“If you feed a ton of data into an LLM, well, that LLM has got to do a lot of work. It's inefficient. Back to utilization. It's not doing useful compute.”