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AI Infrastructure
28 articles tagged AI Infrastructure, newest first.
VMware Explore 2026: Broadcom Bets on Private AI
VMware Explore 2026 centered on Private AI Cloud, AI Factory, agent governance, and private-cloud economics, clarifying Broadcom's VCF strategy.
Nvidia AI Factories: Why Revenue Sharing Hit Pause
Nvidia's 2026 AI cloud financing model paired take-or-pay guarantees with revenue sharing, then some deals paused amid internal competition concerns.
Germany Data Centers: 120 Projects, 5 GW at Stake
A new map tracks 120 German data center projects with at least 5 GW of planned grid capacity, exposing the power and planning challenge.
Datacenter Cooling: Can Waste Heat Replace Power?
A 2026 prototype used waste heat to drive solid-state cooling, but its 4.0 K device-level result is still far from datacenter deployment.
DayOne Confidentially Files for a $5 Billion US Data Center IPO
DayOne Data Centers has confidentially filed for a US initial public offering that could raise about $5 billion, according to [Bloomberg reporting](http...
OpenAI Is Hiring a Power Trading Lead for Its Data Center Portfolio
OpenAI is recruiting a Power Trading Lead to run commodity hedging across its expanding data center power portfolio.
Why is GPU utilization low, and how can you identify whether the bottleneck is compute, network, storage, or data loading?
Low GPU utilization usually means the accelerator is waiting for something.
How can multiple teams or tenants securely share expensive GPU infrastructure?
Multiple teams or tenants can securely share expensive GPU infrastructure by separating identity, permissions, resource entitlement, workload placement,...
What Is an AI Data Center? Key Differences Explained
An AI data center is a data center designed to turn accelerated computing into AI training, inference, and model services.
GPU Resource Pooling and Scheduling in Kubernetes
GPU resource pooling in Kubernetes means turning a set of physical accelerators into managed capacity that workloads can request through policy instead ...
How to Manage Multi Vendor GPUs and NPUs
The clean way to manage GPUs and NPUs from multiple vendors is to separate the problem into layers: physical discovery, health normalization, resource a...
How can enterprises monitor GPU health, ECC errors, temperature, power consumption, and degraded accelerator cards?
Enterprises should monitor GPU health at the individual card level, then combine hardware errors, temperature, power, clocks, utilization, reset history...
What are GPU quotas, reservations, priorities, and preemption, and when should each be used?
GPU quotas, reservations, priorities, and preemption solve four different scheduling problems.
How can IT teams use peak and off peak electricity pricing to reduce infrastructure operating costs?
IT teams can reduce infrastructure operating costs by matching flexible workload schedules to time-of-use electricity prices.
How can data centers detect stranded capacity caused by power, cooling, network, or storage constraints?
Data centers can detect stranded capacity by comparing nominal free capacity with capacity that is actually deployable for a defined workload or equipme...
How can GPU fragmentation be reduced when scheduling AI workloads?
GPU fragmentation can be reduced by managing accelerators as structured resource pools instead of a flat card count.
How can a CMDB connect servers, GPUs, containers, applications, business services, and owners?
A CMDB can connect servers, GPUs, containers, applications, business services, and owners by treating each as a configuration item or related operationa...
How can companies build a unified operations cockpit for data centers, cloud, networks, and AI infrastructure?
Companies can build a unified operations cockpit by bringing the most important operating indicators from infrastructure, compute, network, storage, mod...
How can companies measure the cost of AI infrastructure by GPU hour, Token, project, tenant, or model?
Companies can measure AI infrastructure cost by combining resource metering and service metering under the same ownership model.
How can organizations manage API keys, rate limits, quotas, routing, and fallback for AI and model services?
Organizations can manage API keys, rate limits, quotas, routing, and fallback by placing those controls at a unified model-service gateway.
How do SRE metrics such as MTTD, MTTR, SLO, error budgets, and burn rate apply to AI and infrastructure operations?
SRE metrics turn AI and infrastructure reliability into something teams can measure and govern.
What is MaaS, and how do model repositories, inference instances, API gateways, and Token metering work together?
MaaS, or Model as a Service, turns an AI model into an operable service that applications can call through a controlled interface.
How can AI infrastructure automatically recover training jobs after a GPU or server failure?
AI infrastructure can automatically recover a training job after a GPU or server failure by combining four things: reliable failure detection, a usable ...
How can companies calculate the total cost of ownership of GPU and AI infrastructure?
Companies can calculate the total cost of ownership of GPU and AI infrastructure by combining the costs of acquiring and operating the infrastructure wi...
How can a data center calculate PUE, WUE, GPU energy consumption, and energy cost per Token?
A data center can calculate PUE by dividing total facility energy by IT equipment energy, WUE by dividing water use by IT equipment energy, GPU energy b...
How do RDMA, RoCE, InfiniBand, network packet loss, and storage performance affect AI training performance?
RDMA, RoCE, InfiniBand, network quality, and storage performance affect AI training because distributed training is a data movement problem as much as a...
MSFT's $7 Billion AI Gamble Feels Bigger Than Tech — It Feels Like the Start of Something You Can't Undo
Microsoft's Fairwater buildout is more than a flashy data center expansion. It signals a shift toward industrial-scale intelligence infrastructure with real consequences for power, concentration, and access.
We Thought AI Needed More Electricity. It Actually Needs Better Electricity.
AI’s power problem is not just about megawatts. It is about volatility, grid instability, and the growing role of software-defined power systems inside modern data centers.