A Guide to Reducing AI Infrastructure Costs

Table of Contents

Executive Summary

AI infrastructure has entered a new era. The shift from petabyte to exabyte scale marks a structural break, not a gradual evolution, in enterprise architecture. The economics of AI have also changed. Inference is increasingly the dominant driver of ongoing infrastructure cost in AI production deployments, with every token generated incurring compute, storage, and power expense.

As costs per token continue to drop, infrastructure efficiency has become mission-critical. Competitive advantage is now defined by cost per million tokens and tokens generated per watt. Inadequate storage performance in real-world clusters often leads to significant GPU underutilization, further undermining cost efficiency.

As AI moves from experimentation to high-volume inference, traditional storage systems built for legacy workloads are increasingly unable to meet the speed, scale, and economics required by production AI. Efficient, exascale-ready architectures can positively impact GPU utilization, power allocation, and total cost of ownership. The organizations that get this right won’t just reduce infrastructure costs, they’ll redefine their AI profit margins.

This white paper identifies four key operational issue areas that challenge an organization’s ability to improve efficiency and offers architectural solutions to help resolve them. Executives may leverage this paper to help ensure the speed, scale and economics required of their ecosystems over time and define what it means to be “exascale-native”:

1. Linear capacity scale
2. Linear performance scale
3. Storage density
4. Power efficiency

Legacy architectures often break down beyond ~100 PB, with namespace fragmentation, utilization cliffs, and mounting admin overhead. The engineering requirements and solutions proposed here are derived from our experience with real-world AI systems that process billions of tokens per day across inference, agentic reasoning, simulation, and observability pipelines. In this token-centric AI economy, storage is no longer a back-end cost center, it is a strategic lever for revenue growth, competitive pricing, and long-term viability at scale. Storage, once an afterthought, is now a primary economic and performance lever.

Organizations that have taken advantage of the principles presented here have seen materially higher GPU utilization and 30–50% relative improvement in some deployments when storage bottlenecks are removed.

MinIO AIStor ExaPOD was designed to provide a solution for these challenges.

The Challenge

AI has crossed a structural inflection point, from research to real-time, high-volume inference. For AI-native products, economics are increasingly infrastructure-driven. Tokens are now the currency of AI: every inference carries a direct cost in compute, I/O, and power. While training is a one-time capital investment, inference creates ongoing operational expense, placing pressure on infrastructure to scale cost-effectively. The ability to generate, price, and profit from tokens - AI’s new unit of value - depends on how well your architecture scales.

In this token-driven economy, GPUs must be constantly fed to stay productive; every I/O stall or power inefficiency increases cost per token and erodes competitiveness, impacts business agility, time to production (TTP) and total cost of ownership (TCO).

Modern inference patterns, long-context retrieval, streaming agents, and high-concurrency queries, magnify the impact of storage throughput, latency, and power use. And increasingly, it’s storage that determines whether your GPUs run at 90% utilization or sit idle waiting for data. What once worked at petabyte scale now creates hidden bottlenecks under modern AI workloads, where models generate billions of tokens daily across multimodal inputs. Here’s what matters:

Cost per million tokens is the new north-star metric. Storage directly affects it by gating GPU throughput.Power, not space, is the hard limit. Storage that consumes 4× more watts leaves GPUs unplugged. Many legacy storage architectures begin to exhibit namespace fragmentation, performance cliffs, and rising operational overhead beyond ~100 PB and unsustainable operational overhead.

This white paper presents four key issue areas for consideration to help ensure the speed, scale and economics required for success. It further helps to define what it means to be “exascale-native” and offer commensurate architectural solution principles to overcome them These issue areas are:

As organizations scale beyond 100 PB, only exascale-native storage systems designed with these four issues in mind can meet the economic and operational demands of AI at scale.

Exascale-native storage architecture can reshape the economics of AI infrastructure by turning storage from a sunk cost into a lever for revenue acceleration. Over five years, this translates to multi-million dollars in savings at the exabyte level, capital that can be reinvested in GPU compute to expand token generation capacity or reduce inference pricing to capture market share. The questions we hear Data Leaders asking their teams are:


• Are we losing performance today, and if so, where?
  
  • Token throughput to I/O?
  • Power inefficiency?
  • Both?
• How would our current architecture handle 1 EiB and beyond?
  
  • What is our architecture model at the scale we have today
  • What does that architectural Model look like at 10x growth
• How fast could we test at a 10x scale for future/contingency purposes?
  
  • What would that architecture look like, if different from what we have?
  • How long would it take and what would the cost/benefit look like?

More critically, by reducing watts consumed per petabyte, more power is available for compute. Underperformance in storage not only inflates infrastructure costs but creates hidden losses by starving GPUs, slowing training, which in extreme cases, can effectively double cost per token in under-utilized clusters. The right storage stack unlocks operational leverage, reduces TCO, and maximizes revenue from every dollar and watt invested.

The shift to token-driven infrastructure economics is already underway. Early movers will win on cost, speed, and market share. Everyone else will be priced out.

Exascale Architecture Considerations

Enterprise AI relies on GPU clusters that cost tens to hundreds of millions of dollars, yet many organizations report utilization rates of just 50–70%, far below the 90%+ needed to justify these investments. The leading cause: storage bottlenecks that delay data delivery, reduce token throughput, and inflate cost per token. Below is further detail on these issues:

Issue 1: Linear Capacity Scale

Most traditional enterprise storage architectures were never designed for 100+ Petabyte scale and often break down operationally and economically at that point. This creates operational and economic risks as AI workloads grow.

For example, namespace fragmentation forces data to span multiple systems, increasing complexity, latency, and engineering burden, especially when inference quality depends on seamless access to petabytes of data. When legacy platforms hit architectural ceilings (e.g., node count or capacity limits), re-platforming can take years and disrupt token generation. Many systems suffer performance cliffs at 70–85% utilization, driving costly overprovisioning, often 30–40% extra capacity, just to maintain baseline performance.

By contrast, exascale-native architecture achieves linear capacity scaling through modular, stackable blocks, each with its own compute, storage, and networking. These units eliminate centralized metadata bottlenecks and present a single namespace across all blocks, critical for inference systems accessing vast, distributed knowledge bases.

Issue 2: Linear Performance Scale

Training workloads require sustained high-throughput reads across petabytes; checkpointing demands multi-terabyte burst writes. Inference needs low-latency random reads for thousands of fine-grained object fetches per query. Inadequate storage performance leads to idle GPUs, directly reducing throughput and inflating token cost.

Linear performance scale can be solved by ensuring throughput, latency, and concurrency remain consistent as capacity grows. Every node contributes bandwidth, eliminating shared bottlenecks. Data is automatically distributed across the system, enabling massively parallel access. Median and tail latencies stay stable, even under extreme concurrency, ensuring inference performance does not degrade with scale. Network throughput also scales linearly, preventing network-induced GPU starvation.

Issue 3: Storage Density

Modern AI workloads are growing exponentially, generating billions of tokens daily, yet data center expansion remains slow and capital-intensive, with facility buildouts taking 24–36 months. This creates a hard limit on how much token-generating infrastructure can be deployed within existing power, space, and cooling envelopes.

Extreme storage density has emerged as a critical economic lever. Exascale-native architectures can deliver 36 petabytes of usable capacity per rack at just 900 watts per petabyte (including cooling). In a typical enterprise facility with limited available capacity, this can be the difference between deploying one exabyte of AI-ready storage today or waiting up to two years for new infrastructure. Organizations that embrace high-density, power-efficient storage can scale faster, generate more tokens per megawatt, and establish early market dominance, while slower-moving competitors face cost overruns, service delays, and missed revenue windows in rapidly evolving token-based AI markets.

Issue 4: Power Efficiency

Power efficiency has become the defining constraint in AI infrastructure, as modern data centers are no longer limited by space but by how many tokens can be generated per megawatt. With GPUs consuming up to a megawatt per cluster, every watt spent on inefficient storage is a watt not fueling token generation, and therefore lost revenue.

Exascale-native storage architectures dramatically shift the economics by freeing up power budgets to deploy hundreds more GPU servers within the same facility envelope. In a representative 1 EiB deployment assuming $0.10/kWh electricity, PUE of 1.3, ~900 W/PiB optimized storage, this efficiency can unlock an additional 1.1 exabytes of storage or 450 GPU servers, directly increasing token output without expanding footprint or delaying time-to-revenue.

This translates to >$17M in avoided power + power-infra cost over five years, capital that can be reinvested in compute or used to undercut competitors in per-token pricing. While sustainability benefits and ESG alignment are added advantages, the business case is clear: power-efficient storage improves cost per million tokens, maximizes infrastructure ROI, and strengthens competitive position in a transparent, high-volume inference market.

Solution: MinIO AIStor ExaPOD

Solution 1: Linear Capacity Scale - Growth Without Disruption

MinIO AIStor ExaPOD is designed specifically to address these challenges. The validated reference design For ExaPOD includes 32 racks and 640 servers delivering 1 EiB usable capacity using standard hardware, supporting training data, embeddings, observability, and vector databases. The system maintains availability even during rack failures, thanks to rack-scale fault domains. With software-defined con

These units eliminate centralized metadata bottlenecks and present a single namespace across all blocks, critical for inference systems accessing vast, distributed knowledge bases.

The Benefit: sustainable and flexible linear capacity and scalability with no disruption, increasing reliability.

Solution 2: Linear Performance Scale – Keeping GPUs Saturated and Tokens Flowing

Modern CPU architecture is critical to sustaining this performance. Systems built with Intel Xeon 6781P processors provide 80 cores, 136 PCIe Gen5 lanes, and AVX-512 acceleration. This enables full NVMe and 400GbE throughput without CPU contention, while accelerating erasure coding and encryption inline, freeing power and resources for token-generating GPU workloads.

MinIO AIStor ExaPOD deployed in a reference design with 640 servers and 1 EiB of capacity delivers 19.2 TB/s read throughput. This supports real-time AI workloads at scale with predictable, low-latency performance ensuring fast time-to-first-token for high-concurrency production inference.The Benefit: predictable, scalable performance that maximizes token output per dollar invested, unlocking full ROI from enterprise AI infrastructure.

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Solution 3: Extreme Storage Density – The Space and Power Constraint

Exascale-native storage architectures dramatically shift the economics by freeing up power budgets to deploy hundreds more GPU servers within the same facility envelope.

Storage operating under 1 kW per PiB unlocks capacity for more GPUs within fixed power envelopes, allowing infrastructure to scale without hitting energy ceilings. When combined with next-generation GPU architectures capable of 10x higher throughput per MW, power-efficient storage becomes a direct multiplier of both infrastructure ROI and token-generation capacity.

MinIO AIStor ExaPOD’s exascale-native architectures can deliver 36 petabytes of usable capacity per rack at just 900 watts per petabyte (including cooling). In a typical enterprise facility with limited available capacity, this can be the difference between deploying one exabyte of AI-ready storage today or waiting up to two years for new infrastructure.

The Benefit: Faster Time-to-Production (TPP), reduced hardware, data center footprint and power consumption costs that lower overall TCO.

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Solution 4: Power Efficiency – Maximizing Tokens Per Watt

In a representative MinIO AIStor ExaPOD with 1 EiB deployment assuming $0.10/kWh electricity, PUE of 1.3, ~900 W/PiB optimized storage, this efficiency can unlock an additional 1.1 exabytes of storage or 450 GPU servers, directly increasing token output without expanding footprint or delaying time-to-revenue.

This translates to >$17M in avoided power + power-infra cost over five years, capital that can be reinvested in compute or used to undercut competitors in per-token pricing.

While sustainability benefits and ESG alignment are added advantages, the business case is clear: power-efficient storage improves cost per million tokens, maximizes infrastructure ROI, and strengthens competitive position in a transparent, high-volume inference market.

This power efficiency enables organizations to reallocate megawatts of saved capacity to GPU compute, unlocking hundreds of additional servers without new construction.

Conclusion

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Solution 3: Extreme Storage Density – The Space and Power Constraint

The AI era has ushered in a new economic model where infrastructure is no longer just a cost center, it’s a revenue engine, with token generation as the core metric. As AI transitions from experimentation to scaled inference, storage infrastructure becomes a primary determinant of profitability.

Exascale-native architectures, defined by linear scalability, high density, power efficiency, and predictable performance, enable organizations to maximize token-generation ROI across power, cost, and data center footprint. Critically, efficient storage eliminates bottlenecks that throttle GPU utilization, ensuring every dollar invested in compute infrastructure delivers maximum output.

Organizations that treat infrastructure as an integrated system, where storage, compute, and network are optimized for token throughput, will lead in markets where pricing is measured in cents per million tokens. The time to architect for token economics is now. The organizations that act will define the next decade of AI competitiveness; those that wait will be priced out by infrastructure they can no longer afford to scale.

MinIO AIStor ExaPOD is the right solution at the right time. What “Exascale-Native” Economics with MinIO AIStor ExaPOD Looks like at 1 EiB:

• In MinIO’s ExaPOD reference architecture, modeled economics land at ~$4.55–$4.60 / TiB-month (usable) all-in TCO at 1 EiB scale
• In this design, storage power draw is on the order of ~900 W / PiB (including cooling) -> several-fold more GPUs per MW vs. legacy designs

The Benefits: unlocking full enterprise AI infrastructure ROI and reduced TCO by delivering: Improved cost per million tokens via predictable, scalable performance that maximizes token output per dollar invested.

• Improved cost per million tokens via predictable, scalable performance that maximizes token output per dollar invested.
• Freeing up power budgets to deploy hundreds more GPU servers within the same facility envelope.
• Faster Time-to-Production (TPP) as a result of “exacale-native” linear-scaling capacity & performance architecture by reducing dev & deploy cycles.
• Reduced hardware, data center footprint costs that lower overall TCO.
• Strengthened competitive position in a transparent, high-volume inference market.
• Reduced operational overhead: 3–5 admins vs. 15–30 for comparable legacy environments.