As demand for artificial intelligence continues to skyrocket, so does the need for scalable, high-performance compute infrastructure. However, traditional hyperscale public cloud platforms are becoming an expensive bottleneck. Enterprise teams running large AI models and inference workloads are discovering that centralized cloud providers—like AWS, Azure, and Google Cloud—can no longer deliver the agility, affordability, or global reach that AI demands.

This has paved the way for a new paradigm: distributed cloud infrastructure. Built to power the next wave of AI, this model delivers enterprise-grade GPU compute at dramatically lower prices, with global coverage and no hidden fees.

Why Traditional Public Clouds Fall Short for AI Infrastructure

Legacy cloud services were designed for general-purpose applications, not for modern machine learning and inference workloads. As a result, enterprises relying on centralized cloud infrastructure face mounting challenges:

1. High GPU cloud costs: H100 instances can exceed $30,000/month or $4.00/hour—pricing that quickly scales out of control
2. Hidden fees: Bandwidth, egress, and storage often come with unpredictable surcharges
3. Inconsistent performance: Virtualization and shared tenancy degrade GPU efficiency, introducing “noisy neighbor” issues
4. Limited data center availability: Centralized infrastructure creates latency and compliance gaps for global AI deployments

These limitations prevent teams from scaling models effectively or running cost-efficient inference workflows across borders.

The Distributed Cloud Advantage: A Smarter GPU Infrastructure Model

Distributed GPU cloud infrastructure represents a new approach to AI compute. Instead of concentrating resources in a few hyperscale data centers, distributed platforms aggregate compute from globally dispersed sources—including local and regional data centers, enterprises, and emerging AI infrastructure contributors.

Here’s why more AI-driven businesses are migrating to this model:

1. Lower Cost of GPU Rental

The competitive nature of distributed compute networks results in significantly lower pricing. Most providers in this space offer H100-class GPU access for $12,500 to $15,000 per machine per month, or $1.74 to $2.08 per hour per GPU—massive savings over the centralized cloud average of $4.00+ per hour per GPU.

Aethir, one of the leading providers of distributed GPU infrastructure, delivers even more value with enterprise-grade H100 GPUs at just $1.49/hour/GPU. That’s approximately $8,700/month for 24/7 access to dedicated machines—up to 90% cheaper than some traditional providers.

These rates are fully transparent, including high-speed storage and bandwidth with no hidden egress or networking charges.

2. Global Availability and Latency Reduction

By tapping into a global network of GPU clusters, distributed platforms allow AI workloads to run closer to users or data sources. This helps minimize latency, simplify compliance, and enables multi-region AI model deployment in underserved or emerging markets.

3. Flexible, CapEx-Free Scalability

With no vendor lock-in or long-term contracts, distributed cloud infrastructure allows enterprises to scale GPU usage dynamically—without investing in on-prem hardware or committing to rigid centralized pricing models.

Data based on 730-hour monthly usage. Prices include compute only unless otherwise noted.

Aethir: Enterprise-Grade Distributed GPU Infrastructure for AI

Aethir is a performance-first distributed cloud platform engineered for AI training, inference, and simulation workloads. Its infrastructure follows NVIDIA’s  reference architecture and includes:

1. Bare-metal H100, H200 and B200 access with no virtualization overhead
2. Infiniband and RoCE fabrics for high-throughput, low-latency GPU communication
3. NVMe storage and full-stack customization
4. Global GPU node availability in 95 countries
   

Unlike traditional clouds, Aethir offers flat, predictable pricing with no bandwidth or data movement fees. AI teams get the same performance they’d expect from a top-tier enterprise cloud—at a fraction of the cost.

AI Use Cases in Action: Real Results from Aethir Customers

Inferium: Scaling Verifiable Inference

Inferium, a platform focused on transparent AI inference, used Aethir to scale their infrastructure affordably. By deploying bare-metal GPUs in Aethir’s South Korea facility, Inferium processes over 200,000 inference requests while supporting 280,000+ users. With GPU pricing under control, they redirected budget toward human evaluation pipelines and Proof-of-Inference development.
Read more: Inferium + Aethir Case Study

OpenLedger: Low-Latency Data Intelligence

OpenLedger needed fast, flexible AI compute to support inference across decentralized data networks. Aethir’s global GPU infrastructure allowed them to deploy services closer to users and cut cloud costs significantly. With no egress charges and predictable performance, OpenLedger scaled faster and more efficiently than with any centralized provider.
Read more: OpenLedger + Aethir Case Study

The Future of AI Infrastructure Is Distributed

As AI use cases become more complex and global, legacy cloud infrastructure is no longer sufficient. The future of AI compute must be cost-efficient, performance-driven, and geographically scalable. That’s precisely what distributed cloud infrastructure—and especially Aethir—delivers.

If your organization depends on high-performance GPU workloads, the infrastructure you choose can either limit or accelerate your innovation. Distributed cloud is the infrastructure AI was meant to run on.

For more details on Aethir’s latest innovations, check our official blog section.

To explore our GPU offerings, check our enterprise AI section.