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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
Singapore-based DayOne Data Centers has confidentially filed for a US IPO that could raise about $5 billion, according to Bloomberg.
OpenAI Is Hiring a Power Trading Lead for Its Data Centers
OpenAI is recruiting a Power Trading Lead to run commodity hedging across its expanding data center power portfolio.
Why Is GPU Utilization Low? Compute, Network, Storage or Data Loading
How to tell whether low GPU utilization comes from compute, network, storage, data loading or scheduling, using one shared timeline of metrics.
How Multiple Teams and Tenants Can Securely Share GPU Clusters
How to share GPU clusters across teams and tenants: tenant and project boundaries, quotas, priorities, GPU slicing, metering, audit and vendor access.
What Is an AI Data Center? Key Differences Explained
How an AI data center differs from a traditional one, and what it has to manage: power, cooling, networking, storage, metrics and Kubernetes.
GPU Resource Pooling and Scheduling in Kubernetes
How Kubernetes schedules GPUs with device plugins and DRA, and how Kueue quotas, resource classes, topology and health checks turn them into a shared pool.
How to Manage Multi Vendor GPUs and NPUs in One Platform
Managing GPUs and NPUs from several vendors: a common inventory, Kubernetes device plugins and DRA, resource pools, health aware scheduling and quotas.
How to Monitor GPU Health: ECC, Temperature, Power and Degraded Cards
Monitor GPU health per card: ECC errors, temperature, power and clocks, how to define a degraded accelerator, and how health should drive scheduling.
When to Use GPU Quotas, Reservations, Priorities and Preemption
GPU quotas, reservations, priorities, and preemption solve four different scheduling problems.
Cut Infrastructure Costs With Peak and Off-Peak Pricing
How to shift flexible AI and batch workloads into off-peak tariff windows, calculate the saving, and protect SLOs, deadlines and capacity along the way.
Detecting Stranded Data Center Capacity, Power to Storage
How to find stranded data center capacity by comparing free space, power, cooling, network and storage headroom against a real deployment profile.
How to Reduce GPU Fragmentation When Scheduling AI Workloads
GPU fragmentation can be reduced by managing accelerators as structured resource pools instead of a flat card count.
How a CMDB Links Servers, GPUs, Containers, Services and Owners
How to model servers, GPUs, containers, applications, business services and owners in a CMDB with typed relationships for impact, scheduling and cost.
Unified Operations Cockpit for Data Centers, Cloud, Networks and AI
How to build one operations cockpit for data centers, cloud, networks and AI that reuses existing metrics and drills down to the records behind them.
AI Infrastructure Cost by GPU Hour, Token, Project, Tenant, Model
Companies can measure AI infrastructure cost by combining resource metering and service metering under the same ownership model.
API Keys, Rate Limits, Quotas, Routing and Fallback for AI Services
Organizations can manage API keys, rate limits, quotas, routing, and fallback by placing those controls at a unified model-service gateway.
MTTD, MTTR, SLOs and Error Budgets for AI Infrastructure
How MTTD, MTTR, SLOs, error budgets and burn rate apply to AI inference, training and infrastructure, with formulas, example targets and dashboard metrics.
What Is MaaS? Repositories, Inference, API Gateways and Token Metering
MaaS, or Model as a Service, turns an AI model into an operable service that applications can call through a controlled interface.
Automatic Training Job Recovery After a GPU or Server Failure
How AI infrastructure recovers training jobs after a GPU or server failure: detection, checkpoints, isolating bad cards, replacement capacity, validation.
Total Cost of Ownership for GPU and AI Infrastructure
What goes into GPU and AI infrastructure TCO: hardware, card hours, energy, network, storage, maintenance, idle capacity and cost per Token.
How to Calculate PUE, WUE and GPU Energy Cost per Token
How to calculate PUE, WUE, GPU energy and energy cost per Token, with worked formulas, measurement boundaries and shared GPU cost allocation.
RDMA, RoCE and InfiniBand for AI Training Storage
Why distributed AI training slows down: how RDMA, InfiniBand, RoCE, packet loss, latency and storage leave GPUs waiting, and how to trace the cause.
MSFT's $7 Billion AI Gamble Feels Like Something You Can't Undo
Microsoft's $7 billion Fairwater AI bet and what people say about its scale, jobs, energy use, and who ends up controlling AI infrastructure.
AI's Power Problem: Grid Volatility as Much as Megawatts
AI data centers need more than new gigawatts. Volatile GPU loads strain the grid, so power visibility and software control matter as much as capacity.