Deploying AI applications across diverse consumer hardware has traditionally forced a trade-off. You can optimize for specific GPU configurations and achieve...

Deploying AI applications across diverse consumer hardware has traditionally forced a trade-off. You can optimize for specific GPU configurations and achieve peak performance at the cost of portability. Alternatively, you can build generic, portable engines and leave performance on the table. Bridging this gap often requires manual tuning, multiple build targets, or accepting compromises.

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Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want...

Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want to run conversational AI agents, multimodal perception, and high-level planning directly on the vehicle or robot – where latency, reliability, and the ability to operate offline matter most. While many existing LLM and vision language…

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For machine learning engineers deploying LLMs at scale, the equation is familiar and unforgiving: as context length increases, attention computation costs...

For machine learning engineers deploying LLMs at scale, the equation is familiar and unforgiving: as context length increases, attention computation costs explode. Whether you’re dealing with retrieval-augmented generation (RAG) pipelines, agentic AI workflows, or long-form content generation, the complexity of attention remains a primary bottleneck. This post explains a technique known as…

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As AI models get larger and architectures more complex, researchers and engineers are continuously finding new techniques to optimize the performance and...

As AI models get larger and architectures more complex, researchers and engineers are continuously finding new techniques to optimize the performance and overall cost of bringing AI systems to production. Model optimization is a category of techniques focused on addressing inference service efficiency. These techniques represent the best “bang for buck” opportunities to optimize cost…

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For the past 25 years, real-time rendering has been driven by continuous hardware improvements. The goal has always been to create the highest fidelity image...

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Over the past few years, AI inference has evolved from single-model, single-pod deployments into complex, multicomponent systems. A model deployment may now...

Over the past few years, AI inference has evolved from single-model, single-pod deployments into complex, multicomponent systems. A model deployment may now consist of several distinct components—prefill, decode, vision encoders, key value (KV) routers, and more. In addition, entire agentic pipelines are emerging, where multiple such model instances collaborate to perform reasoning, retrieval…

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Research by 3E and Statkraft has used a new performance measure for solar trackers to uncover “alarming” evidence of a gap between claimed and actual performance.

Originally posted on Enterprise Times.

As blockchain adoption moves beyond crypto-native startups into the enterprise mainstream, the infrastructure demands of validator nodes are becoming a strategic consideration.

Across industries, enterprises are exploring blockchain not for speculation but for operational transparency and data integrity. Financial institutions use private and consortium chains to streamline settlement and compliance. Logistics companies apply blockchain to track provenance and supply chain authenticity, in addition, healthcare and government sectors are testing it for secure records management and digital identity.

This shift from experimentation to integration is prompting IT leaders to evaluate how validator infrastructure fits within existing enterprise standards for performance, reliability, and governance.

Validators keep blockchain networks honest. They confirm transactions, secure consensus, and maintain the integrity of digital assets in motion. For organizations participating in staking or building on decentralized protocols, validator performance is not optional. Reliability, uptime, and security directly affect financial outcomes and brand trust.

While cloud computing has long been the default for fast deployment, validator workloads have unique requirements that challenge shared virtual environments. High latency, unpredictable resource allocation, and compliance concerns can undermine both performance and profitability. To achieve the scale and precision modern networks demand, enterprises are re-evaluating their infrastructure foundations.

To be clear, cloud infrastructure has earned its place in enterprise IT for good reason. Rapid provisioning, elastic scaling, and minimal upfront investment make it ideal for development environments, variable workloads, and teams that need to move fast without dedicated infrastructure expertise.

For many blockchain applications—particularly in early-stage testing or low-stakes environments—cloud remains a practical choice. The question isn’t whether cloud works, but whether it works well enough for the specific demands of production validator operations where penalties, rewards, and reputation are on the line.

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The post Powering Enterprise Blockchain Validators with Bare Metal Infrastructure appeared first on Data Center POST.

At this year’s infra/STRUCTURE Summit 2025, held at the Wynn Las Vegas, one of the most forward-looking conversations came from the session “From Cloud to Edge to AI Inferencing.” Moderated by Philbert Shih, Managing Director at Structure Research, the discussion brought together a diverse panel of innovators shaping the future of cloud and AI infrastructure: Kevin Cochrane, Chief Marketing Officer at Vultr; Jeffrey Gregor, General Manager at OVHcloud; and Darrick Horton, CEO at TensorWave.

Together, they explored the emergence of new platforms bridging the gap between hyperscale cloud providers and the next wave of AI-driven, distributed workloads.

The Rise of Alternatives: Choice Beyond the Hyperscalers

Philbert Shih opened the session by emphasizing the growing diversity in the cloud ecosystem, from legacy hyperscalers to specialized, regionally focused providers. The conversation quickly turned to how these companies are filling critical gaps in the market as enterprises look for more flexible, sovereign, and performance-tuned infrastructure for AI workloads.

Cochrane shared insights from a recent survey of over 2,000 CIOs, revealing a striking shift: while just a few years ago nearly all enterprises defaulted to hyperscalers for AI development, only 18% plan to rely on them exclusively today. “We’re witnessing a dramatic change,” Cochrane said. “Organizations are seeking new partners who can deliver performance and expertise without the lock-in or limitations of traditional cloud models.”

Data Sovereignty and Global Reach

Data sovereignty remains a key differentiator, particularly in Europe. “Being European-born gives us a unique advantage,” Gregor noted. “Our customers care deeply about where their data resides, and we’ve built our infrastructure to reflect those values.”

He also highlighted OVHcloud’s focus on sustainability and self-sufficiency, from designing and operating its own servers to pioneering water-cooling technologies across its data centers. “Our mission is to bring the power of the cloud to everyone,” Gregor said. “From startups to the largest public institutions, we’re enabling a wider range of customers to build, train, and deploy AI workloads responsibly.”

AI Infrastructure at Scale

Horton described how next-generation cloud providers are building infrastructure purpose-built for AI, especially large-scale training and inferencing workloads. “We design for the most demanding use cases, foundational model training, and that requires reliability, flexibility, and power optimization at the cluster scale.”

Horton noted that customers are increasingly choosing data center locations based on power availability and sustainability, underscoring how energy strategy is becoming as critical as network performance. TensorWave’s approach, Horton added, is to make that scale accessible without the hyperscale overhead.

Democratizing Access to AI Compute

Across the panel, a common theme emerged: accessibility. Whether through Vultr’s push to simplify AI infrastructure deployment via API-based services, OVHcloud’s distributed “local zone” strategy, or TensorWave’s focus on purpose-built GPU clusters, each company is working to make advanced compute resources more open and flexible for developers, enterprises, and AI innovators.

These alternative cloud providers are not just filling gaps — they’re redefining what cloud infrastructure can look like in an AI-driven era. From sovereign data control to decentralized AI processing, the cloud is evolving into a more diverse, resilient, and performance-oriented ecosystem.

Looking Ahead

As AI reshapes industries, the demand for specialized infrastructure continues to accelerate. Sessions like this one underscored how innovation is no longer confined to the hyperscalers. It’s emerging from agile providers who combine scale with locality, sustainability, and purpose-built design.

Infra/STRUCTURE 2026: Save the Date

Want to tune in live, receive all presentations, gain access to C-level executives, investors and industry leading research? Then save the date for infra/STRUCTURE 2026 set for October 7-8, 2026 at The Wynn Las Vegas. Pre-Registration for the 2026 event is now open, and you can visit www.infrastructuresummit.io to learn more.

The post Alternative Cloud Providers Redefine Scale, Sovereignty, and AI Performance appeared first on Data Center POST.

When IT goes down, the hit is immediate: revenue walks out the door, employees grind to a halt, and customers start questioning your credibility. Cloud services are built to prevent that spiral, with redundancy, automatic failover, and cross region replication baked right in. Try matching that with your own data center and you are signing up for massive hardware bills, nonstop maintenance, and the joy of keeping everything powered and patched around the clock. Cloud resilience is not just better. It is on a completely different level.

The High Stakes of Downtime

Your business depends on fast, reliable access to data: whether you’re running an eCommerce platform, financial services firm, or healthcare system. Downtime isn’t just an inconvenience; it’s a financial disaster. Every minute of outage costs businesses an average of $9,000. That’s why companies demand high-availability (HA) and disaster recovery (DR) solutions that won’t fail when they need them most. HA and DR are essential components of any organization’s business continuity plan (BCP).

Dependable access to business data is essential for operational efficiency and accurate decision-making. More organizations rely on access to high-availability data to automate business processes, and ready access to stored data and databases is critical for e-commerce, financial services, healthcare systems, CRM, inventory management, etc. The increased need for reliable data access drives more organizations to embrace cloud computing.

According to Gartner, “Seventy percent of organizations are poorly positioned in terms of disaster recovery (DR) capabilities, with 54% likely suffering from ‘mirages of overconfidence.’” To minimize the risk of costly downtime, cloud customers must shop for reliable data access services.

The Uptime Institute offers four tiers of data center classification for resiliency and redundancy.

• Tier I – The lowest uptime ranking with basic data infrastructure, limited redundancy, and the highest risk of downtime.
• Tier II – Offers additional physical infrastructure redundancy for power and cooling with downtime during maintenance.
• Tier III – Supports concurrent maintenance and provides multiple data disruption paths so components can be removed or replaced without interruption.
• Tier IV – A fault-tolerant infrastructure that guarantees uninterrupted cooling and power, with redundant systems so that no single event will create a failure.

Most on-premises data centers strive for Tier II or III designs. Most customers demand high uptime shop for Tier III and Tier IV services, and the cost and complexity of achieving a Tier IV design are usually left to cloud service providers.

To provide high availability, many cloud computing providers segment their infrastructure into Availability Zones (AZs), with each zone set up to operate independently. Each AZ has one or more data centers with self-contained power, cooling, and networking capabilities to minimize failures. AZs are typically situated close to one another to minimize latency for data replication. They also have redundant infrastructures, so there is no single point of failure within the AZ. Distributing workloads across AZs promotes high availability and alight with Tier III and Tier IV capabilities to minimize downtime.

To calculate uptime, you multiply the availability of every layer of the application infrastructure, starting with the underlying AZ, through the operating system, and then finally the application layer. In order to have the highest availability, architectures allow for applications to “fail over, patch/upgrade, and then fail back.” whether it is across AZs, or during operating system, database patching, and upgrade.

The ability to replicate data across regions is the ideal solution for disaster recovery. Regions can be separated by geographic areas or even continents, but having access to redundant data ensures that applications and workloads remain operational, even in the event of a natural disaster or widespread network failure.

Providing cross-region DR in the cloud is central to the BCP and ensures data availability, with data being asynchronously or synchronously replicated across regions. Cloud DR also includes managed failover, switching traffic to a secondary region if the primary cloud infrastructure fails.

Maintaining cross-region failovers may have some performance tradeoffs. There may be higher latency and costs compared to multi-AZ replication, but the benefits of maintaining continuous operations offset performance drawbacks.

Comparing On-premises Versus Cloud HA and DR

Deciding whether to adopt on-premises or cloud computing data centers is largely a matter of comparing costs and capabilities.

On-premises environments are ideal for users who require absolute control and customization. For example, healthcare and financial services organizations may need full control over hardware and software configurations and data because of security and unique compliance requirements. On-premises data centers also offer greater control over system performance.

Scaling on-premises data centers to support high availability and disaster recovery is expensive, requiring redundant hardware and software, generators, backup cooling capacity, etc. Maintaining a high-performance data infrastructure requires substantial expertise and maintenance, including regular failover testing.

While cloud-based data centers offer less control over configurations, they tend to provide greater reliability. The cloud service providers manage the physical infrastructure, scaling computing power and data storage as needed without installing additional hardware. Service-level agreements ensure data availability and system uptime.

Cloud data centers also offer high availability using strategies such as Availability Zones and disaster recovery using cross-region replication. Most of these services are included with cloud services contracts and are easier to set up than provisioning multiple sites on-premises. Most cloud computing providers also use a pay-as-you-go model, simplifying budgeting and cutting costs.

Many organizations adopt a hybrid strategy, using on-premises data center services for critical computing applications and leveraging cloud computing services for DR and scalability. Adopting a hybrid approach mitigates risk by replicating critical workloads to cloud-based systems, providing DR without duplicating hardware and software. Adopting a hybrid strategy also helps to cut costs for redundant services that are seldom used. It also allows companies to migrate data services to the cloud over time.

In the end, high availability and disaster recovery are not optional; they are the backbone of every modern enterprise. And while hybrid strategies can balance security, compliance, and cost, the cloud remains unmatched when it comes to delivering true resilience at scale. Its built-in redundancy, automatic failover, and cross region replication provide a level of protection that on premises systems simply cannot match without astronomical investment. For organizations that want continuity they can trust, the cloud is not just a viable option. It is the strategic choice.

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About the Author

Bakul Banthia is co-founder of Tessell. Tessell is a cloud-native Database-as-a-Service (DBaaS) platform that simplifies the setup, management, security, and scaling of transactional and analytic databases in the cloud.

The post Cloud Outages Cost You Big: Here’s How to Stay Online No Matter What appeared first on Data Center POST.