Beyond Benchmarks: Architecting a Holistic View of AI Infrastructure

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Author: Denis Avetisyan

A new framework integrates performance, efficiency, and cost across the entire AI stack, offering a pathway to truly optimized and sustainable systems.

This paper introduces a 6×3 taxonomy and Metric Propagation Graph for cross-layer analysis of AI infrastructure, enabling informed decision-making and holistic optimization.

Despite the rapid growth of large-scale AI, infrastructure limitations increasingly constrain progress, yet current metrics remain fragmented across physical, computational, and economic domains. This paper, ‘A Unified Metric Architecture for AI Infrastructure: A Cross-Layer Taxonomy Integrating Performance, Efficiency, and Cost’, addresses this gap by presenting a novel 6×3 taxonomy and Metric Propagation Graph to systematically map and analyze interdependencies across the AI infrastructure stack. This integrated framework enables holistic optimization of energy, carbon emissions, and cost, offering a coherent foundation for informed decision-making in areas like cluster design and lifecycle economic analysis. Will this unified approach unlock new efficiencies and accelerate the development of sustainable, high-performance AI systems?

Decoding the Evolving Landscape of AI Infrastructure

The surge in sophisticated artificial intelligence applications is fundamentally reshaping data center demands, introducing operational complexities previously unseen. Modern AI workloads, particularly those involving large language models and generative AI, require vast computational resources, high-bandwidth interconnects, and specialized hardware accelerators. This necessitates a move beyond conventional infrastructure monitoring, as pinpointing performance bottlenecks within these intricate systems proves increasingly difficult. The sheer scale of these deployments-often involving thousands of GPUs and complex networking topologies-introduces challenges in resource allocation, power management, and fault tolerance. Consequently, organizations are grappling with the need for intelligent infrastructure management solutions capable of dynamically adapting to the fluctuating demands of AI, ensuring optimal performance and minimizing operational overhead.

Current infrastructure monitoring systems, designed for conventional computing workloads, frequently struggle to effectively diagnose performance issues within modern AI deployments. These systems often lack the necessary resolution to identify bottlenecks arising from the complex interplay of GPUs, CPUs, and high-speed networks – a crucial shortcoming given that interconnect performance is now a primary determinant of overall AI application speed and efficiency. The increasing reliance on distributed training and inference necessitates a far more granular approach to monitoring, one that can pinpoint latency spikes and bandwidth limitations within the network fabric itself. Without this level of insight, organizations risk suboptimal resource allocation, hindering the scalability and cost-effectiveness of their AI initiatives and potentially leading to significant delays in model development and deployment.

A Unified Taxonomy for Infrastructure Understanding

The Unified Taxonomy is a 6×3 framework designed to categorize infrastructure metrics. The six layers of this taxonomy are Grid, Facility, Compute, Interconnect, Runtime, and Service Economics, representing the full infrastructure stack from power source to application service. Metrics are further classified within each layer by domain – a categorization not further specified here – enabling granular analysis and consistent reporting. This structure allows for the organization of diverse data points, facilitating a standardized approach to infrastructure performance evaluation and comparison across different environments and technologies.

The Unified Taxonomy establishes a standardized vocabulary for infrastructure metrics, categorizing them across six layers – Grid, Facility, Compute, Interconnect, Runtime, and Service Economics – and within defined domains. This structured approach facilitates consistent evaluation of infrastructure performance from the power source to the application level. By applying a common language, the taxonomy enables organizations to aggregate, compare, and analyze metrics across disparate systems and teams, improving visibility into overall infrastructure health and efficiency. This standardization is crucial for benchmarking, capacity planning, and identifying areas for optimization, moving beyond isolated data points to a comprehensive understanding of interdependencies and resource utilization.

The Unified Taxonomy facilitates comprehensive resource utilization analysis by moving beyond isolated metric evaluations, such as Power Usage Effectiveness (PUE). Instead of focusing on single-layer efficiency, the 6×3 framework allows for the correlation of metrics across the entire infrastructure stack – from Grid to Service Economics. This cross-layer visibility reveals interdependencies; for example, changes in compute resource allocation can be directly linked to facility power draw and overall service economics. By understanding these relationships, operators can optimize resource allocation, identify bottlenecks, and improve overall infrastructure performance based on system-wide impact, rather than localized gains.

Mapping Dependencies with Metric Propagation Graphs

The Metric Propagation Graph is a directed graph where nodes represent infrastructure metrics – such as CPU utilization, network latency, or disk I/O – and edges define dependencies between these metrics. An edge from metric A to metric B indicates that a change in the value of metric A will likely affect metric B. This representation allows for the visualization of how performance issues or resource constraints in lower layers of the infrastructure – for example, a saturated network interface – can propagate upwards, impacting metrics at higher layers, such as application response time. The graph explicitly maps these relationships, enabling operators to understand the chain of dependencies and identify potential points of failure or bottlenecks within the system.

The utilization of a graph-theoretic model for dependency mapping enables the identification of bottleneck root causes by representing infrastructure metrics as nodes and their relationships as edges. Analysis of these graphs reveals how resource constraints in one system component affect others, allowing operators to trace performance degradation to its origin. Current infrastructure trends indicate interconnect latency is increasingly significant; the model explicitly accounts for this by representing interconnect delays as weighted edges, thus quantifying the impact of network performance on application responsiveness. This facilitates proactive identification of latency-induced bottlenecks and enables targeted optimization of interconnect infrastructure to mitigate performance issues.

Operators leverage metric dependency tracing to implement proactive issue resolution by monitoring the flow of data between infrastructure components. This allows for the identification of potential bottlenecks or performance degradations before they manifest as application-level problems. By analyzing dependencies, operators can pinpoint the source of an issue – even if it originates in a seemingly unrelated layer – and implement corrective actions such as resource allocation, configuration adjustments, or code optimization. This preventative approach minimizes downtime, reduces mean time to resolution (MTTR), and ultimately improves the overall user experience by maintaining consistent application performance.

Optimizing for Efficiency and Reliability: A Systems Perspective

A robust approach to physical facility efficiency begins with a detailed understanding of energy flows, traditionally quantified by metrics like Power Usage Effectiveness (PUE). However, a truly optimized infrastructure demands a move beyond singular metrics. The Unified Taxonomy and Metric Propagation Graph provides a means to dissect complex facility operations, identifying specific areas for targeted improvement. This framework doesn’t simply measure what energy is used, but critically, where and how, allowing for granular adjustments to cooling systems, power distribution, and overall facility layout. By propagating metrics beyond PUE to encompass factors like water usage, carbon emissions, and equipment lifespan, the system facilitates a holistic evaluation of efficiency, ultimately driving sustainable and cost-effective operations.

Quantifying computational efficiency often relies on metrics like Floating-point Operations Per Watt (FLOPs/W), a measure of performance relative to energy consumption. However, this work demonstrates that solely maximizing FLOPs/W provides an incomplete picture of true efficiency within AI infrastructure. While increasing computational throughput per watt is beneficial, neglecting other critical components-particularly network performance-can create bottlenecks that negate gains. High-performance computing demands rapid data transfer, and if network bandwidth or latency limits the flow of information to and from processing units, the potential of optimized hardware remains unrealized. Consequently, a holistic approach is necessary, evaluating compute efficiency in conjunction with network capabilities to unlock genuine improvements in overall system performance and reduce operational costs.

The pursuit of efficient and reliable AI infrastructure ultimately converges on a reduced Total Cost of Ownership (TCO), but traditional TCO models often present an incomplete picture. This work proposes a more comprehensive approach, extending beyond initial capital expenditures to incorporate the full lifecycle costs associated with AI systems – including maintenance, upgrades, and eventual decommissioning. Crucially, the framework integrates reliability metrics, acknowledging that downtime and failures represent significant financial burdens. Furthermore, it recognizes the growing importance of sustainability factors, such as energy consumption and carbon footprint, which are increasingly subject to regulatory scrutiny and can dramatically impact long-term operational costs. By holistically evaluating these interconnected elements, a more accurate and actionable TCO assessment is achieved, enabling data centers to make informed decisions that maximize return on investment and minimize environmental impact.

The presented work emphasizes a systemic understanding of AI infrastructure, moving beyond isolated performance evaluations. This holistic approach aligns with the philosophical insights of Georg Wilhelm Friedrich Hegel, who stated, “The truth is the whole.” The 6×3 taxonomy and Metric Propagation Graph detailed in this paper aren’t merely tools for measurement; they represent an attempt to capture the interconnectedness of physical resources, computational processes, and economic considerations. By tracing metric propagation across these layers, the framework facilitates a comprehensive view-a ‘whole’ picture-essential for truly informed optimization and sustainable AI development. The ability to model this system allows for a deeper understanding of the relationships between components, moving beyond superficial observation to identify underlying patterns and drivers of efficiency.

Beyond the Numbers

The presented taxonomy, while offering a structured lens through which to view AI infrastructure, inevitably highlights the gaps in current understanding. The propagation of metrics across layers-from silicon to economics-reveals a surprising scarcity of standardized interfaces. The field assumes a certain transparency in cost attribution, yet this work suggests that a substantial portion remains obscured, modeled through assumptions rather than direct observation. Future research must prioritize the development of instrumentation capable of tracing resource consumption with greater fidelity, demanding a shift from aggregate metrics to granular, event-based data collection.

The model implicitly acknowledges the inherent tension between performance, efficiency, and cost. However, quantifying the acceptable trade-offs remains elusive. Is a 10% gain in performance worth a 20% increase in energy consumption? The answer, predictably, is context-dependent, but a formal framework for navigating these compromises-perhaps borrowing from multi-objective optimization techniques-could prove invaluable. The pursuit of “optimal” infrastructure is a fool’s errand; the goal should be adaptable infrastructure, capable of reconfiguring itself to meet evolving demands.

Ultimately, the true test of this taxonomy lies not in its descriptive power, but in its predictive capability. Can it anticipate bottlenecks before they manifest? Can it identify emergent behaviors arising from complex interactions? These are not merely engineering challenges; they are exercises in systems thinking. The patterns revealed by these metrics are not just numbers; they are echoes of the underlying physics, the constraints of economics, and the inevitable imperfections of any complex system.

Original article: https://arxiv.org/pdf/2511.21772.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-02 03:23