The traditional maxim in artificial intelligence—”the bigger the data, the better the model”—has long characterized AI development. As we enter 2025, we can append that paradigm with a further understanding of data-centric AI: “The deeper the reasoning, the greater the storage demand.”
Data-centric AI represents a fundamental shift from focusing primarily on model architecture to recognizing that data—its quality, quantity, and accessibility—increasingly determines AI system performance on both training and inference.
We’re witnessing a profound transformation from perception models to sophisticated reasoning systems that maintain complex conversations and analyze entire documents. This evolution demands a new approach to how we architect data storage infrastructure to support these advanced capabilities.
Data-Centric AI and the Evolution of Reasoning
AI has undergone a remarkable evolution—from perception models like AlexNet in 2012, through the generative AI revolution, and now into reasoning systems that fundamentally transform how organizations leverage artificial intelligence. This shift isn’t merely about larger models; it’s about embracing data-centric AI approaches that fundamentally change how AI processes and stores information.
Debunking Inferencing Myths in Data-Centric AI
Several persistent myths about AI inferencing that have led many organizations to underestimate their infrastructure requirements:
- Myth: Inferencing is computationally simple
- Myth: Inferencing can run effectively on CPUs alone
- Myth: Advanced networking isn’t critical for inference workloads
The reality is far more complex. Today’s data-centric AI systems with reasoning capabilities demand specialized infrastructure, particularly when implementing sophisticated technologies like Retrieval-Augmented Generation (RAG) and maintaining conversational context through KV Cache systems.
Understanding KV Cache Growth in Data-Centric AI
Recent industry data suggests that KV cache volumes are expected to grow at an unprecedented rate over the next three years:
- Current KV cache requirements for basic inferencing typically range from 10-100GB per concurrent user for enterprise-scale deployments
- Models with reasoning capabilities are already pushing these requirements to 250-500GB per user
- By 2026, experts project KV cache needs will reach 2-5TB per concurrent user for advanced reasoning models in production environments
This represents a 20-50x increase in storage requirements compared to traditional inferencing models. The primary drivers of this growth in data-centric AI include:
- Larger context windows: Reasoning models must maintain awareness across much larger input spans
- Multi-step reasoning processes: Each inferencing step generates intermediate states that must be cached
- Cross-document references: Advanced reasoning often requires maintaining relationships between multiple sources
- Persistent memory requirements: Reasoning capabilities benefit from maintaining state across multiple user interactions
The RAG Effect on Data-Centric AI Storage
Retrieval-Augmented Generation (RAG) workflows can increase data storage requirements by 10-20x. RAG enhances AI queries by embedding relevant documents into prompts, making responses more contextually accurate but dramatically increasing storage demands.
The challenge isn’t just about raw capacity but about architecting systems that can efficiently store, retrieve, and process this information at scale. Future data-centric AI systems will increasingly communicate in binary rather than converting to and from text repeatedly, further transforming storage architecture requirements.
Per-User Requirements in Multi-User Data-Centric AI
When data-centric AI platforms serve multiple simultaneous users, the storage requirements multiply accordingly:
- An enterprise platform with 1000 concurrent users could require two to five petabytes of high-performance storage
- Cloud service providers supporting thousands of concurrent reasoning sessions could scale far higher
- Even with intelligent resource sharing, the storage demands remain substantial
Data-centric AI systems will increasingly need to process enormous contexts—up to 100,000 tokens for book-length analysis—while maintaining conversational history through sophisticated KV cache implementations.
S3 API: The Ideal Foundation for Data-Centric AI Storage
Object storage platforms that implement the S3 API are exceptionally well-suited for data-centric AI environments because they deliver the perfect combination of scalability, performance, and standardization required by reasoning-capable systems.
The S3 API provides a consistent, RESTful interface that enables seamless integration with AI processing frameworks while supporting the massive parallelism needed for efficient data retrieval during inferencing. This standardized approach allows AI pipelines to treat storage as a programmable resource—essential when managing the exponentially growing KV cache volumes and document repositories that power RAG workflows.
Unlike traditional file systems that struggle with the capacity demands of data centric AI, S3-compatible storage excels at handling the diverse data patterns generated by reasoning AI, from tiny metadata fragments to massive context windows. Additionally, the policy-based data management capabilities inherent in the S3 model enable organizations to implement automated lifecycle policies that intelligently tier AI data based on access patterns, ensuring optimal performance while controlling costs as data-centric AI deployments scale to petabyte levels.
Strategic Recommendations for Data-Centric AI Infrastructure
Organizations preparing for this new era of data-centric AI should consider several strategic approaches:
- Adopt object storage with native S3 compatibility: Cloudian HyperStore provide the scalability and performance characteristics needed for expanding KV cache volumes
- Leverage GPU-adjacent storage: Positioning storage resources in close proximity to NVIDIA GPU accelerators minimizes data movement overhead
- Plan for exponential growth: Storage infrastructure decisions should accommodate at least 3-5 years of projected growth in KV cache requirements
- Explore hybrid on-premises/cloud solutions: Balance the performance benefits of local storage with the elasticity of cloud resources
- Consider tiered storage architectures: Create systems that automatically migrate KV cache data across performance tiers based on access patterns and criticality
Conclusion
The transition to data-centric AI with reasoning capabilities represents a fundamental shift in storage requirements. Organizations that anticipate and plan for the exponential growth in data processing and storage demands will be best positioned to capitalize on the advanced capabilities these models offer.
By combining performant object storage solutions with accelerated computing platforms, enterprises can build the infrastructure foundation needed to support the next generation of data-centric AI reasoning capabilities. As we continue to push the boundaries of AI intelligence, our data storage strategies must evolve in tandem, ensuring that our infrastructure empowers rather than constrains these remarkable new capabilities.
Learn more at cloudian.com/ai-workflows.
Click here for a free trial.
