TEN-EN

What is Kubernetes? Learn how it automates container deployment, scaling, and recovery, how it differs from Docker, and why AI and GPU teams rely on it.

What separates AI training and inference? A clear guide to both stages, their different GPU usage patterns, why their infrastructure needs differ, and how to run both efficiently on one GPU infrastructure.

Should you build your own GPU platform on Kubernetes, or buy one? License cost says build, but 3-year TCO with hidden costs tells a different story.

What is a token in an LLM? A clear guide to what tokens are, how they differ from words, input vs output tokens and the context window, why tokens became the unit of AI cost and performance, and the GPU infrastructure behind token throughput.

GPU-Util reads 90% but training is slow. Why? Beyond the utilization trap, here are the 7 metrics that actually matter and how to read them in layers.

What is a foundation model? A clear guide to the model that's pretrained on broad data and adapted to many tasks — its relationship to LLMs, the pretraining and fine-tuning process, and the GPU infrastructure enterprises need to use it.

How do you split the cost of a shared GPU cluster across teams? From showback to chargeback, here is a practical 4-step process for allocating GPU spend.

Why is judging your infrastructure by GPU-Util alone so risky? Beyond the utilization trap, here are the 5 metrics that actually matter in operations.

Is NVIDIA MIG enough for sharing GPUs? Compare MIG's limits — fixed profiles, a max of seven instances, GPU restrictions — against software partitioning with 1% granularity and dynamic reallocation, and see which fits your environment.

What separates NVIDIA's H100, H200, and B200? Compare HBM capacity and bandwidth, architecture (Hopper vs Blackwell), FP4 support, and real specs — plus which GPU to pick for LLM training and inference workloads.

What defines a data center Tier? A clear guide to the Uptime Institute's Tier 1–4 classification, uptime and annual downtime, redundancy concepts like N+1 and 2N, and which tier AI data centers actually need.

How do you fairly allocate shared GPU costs across teams? A FinOps guide to the difference between chargeback and showback, what to measure, and the practical steps to build a GPU cost allocation system.

Build GPU infrastructure on-prem or rent it in the cloud? Compare the two on TCO, data security, scalability, and utilization — plus when a hybrid strategy makes the most sense.

PUE (Power Usage Effectiveness) shows how efficiently a data center uses power. Here's what PUE means, how it's calculated, why it worsens in AI data centers, and how to improve power efficiency in high-density GPU environments.

GPU utilization of 95% can still mean your GPUs are barely working. Here's why nvidia-smi's utilization metric doesn't reflect actual compute, what MFU really measures, and how to monitor GPU efficiency correctly.

Average GPU utilization of just 5% isn't a GPU shortage problem. Here are the five real causes — idle allocation, monopolized cards, no scheduling — plus how to diagnose and fix each

What is VRAM and why does it matter for running LLMs? A simple guide to GPU memory vs system RAM, how to estimate VRAM by model size, and what to do when you run out.

Why do GPU jobs sit pending when GPUs are still free? A clear look at how Kubernetes GPU scheduling works, the limits of the default scheduler, the fragmentation problem, and what efficient GPU placement requires.

What is a neocloud? A clear look at GPU-focused clouds built for AI workloads. how they differ from hyperscalers, key players like CoreWeave, and the operations layer that decides profitability.

The AI data center bottleneck is shifting from GPUs to power. Here's why transformer lead times now stretch past three years and how to run more AI on the power you already have.

Neoclouds are GPUaaS clouds for AI workloads. After the GPU race, profitability hinges on the operational layer — utilization, multi-tenancy, billing, and automation.

No matter how many GPUs you buy, there's no profitability without power control. — we break down AIPub's GPU power optimization strategy.

If you chose your GPU based on FLOPs/$, you'll regret it when inference bills arrive. We break down Blackwell Ultra's cost-per-token revolution and what it means for infrastructure operations.

Learn what a Modular Data Center (MDC) is, how it differs from containerized data centers, and why MDC are becoming a practical deployment model for high-density AI infrastructure.

Explore why traditional data centers are reaching their limits in the AI era, as GPU power density, cooling requirements, and construction timelines reshape AI infrastructure strategy.

Traditional infrastructure operations can no longer control resource contention or security risks. Here's why access control must extend beyond GPUs to the entire stack.

Low GPU utilization isn't caused by the structural limits of time-slicing. See how spatial partitioning fundamentally solves these problems.

What is Docker and why is it essential for AI and MLOps? Learn container architecture, runtime structure, and how to manage GPUs efficiently in container environments.

DiLoCo, Streaming DiLoCo, and Decoupled DiLoCo cut communication overhead in distributed LLM training — and what it means for multi-cluster GPU ops.

At KubeCon EU 2026, NVIDIA donated GPU scheduling tools to CNCF. With 80% of AI workloads now running on Kubernetes, GPU scheduling has become essential infrastructure.

Thinking of building a GPU cluster? Check these 5 essential factors before scaling your AI infrastructure, from distributed training to scheduling and resource optimization.

What is NVIDIA MIG? Learn how Multi-Instance GPU enables efficient GPU partitioning, improves utilization, and reduces AI infrastructure costs with real-world strategies.

GPUs alone are not enough for AI infrastructure. Learn why storage and network fabric are critical to performance, and how to design a balanced AI system.

Why are expensive GPUs sitting idle? Learn 5 practical strategies for GPU scheduling, fractional usage, and AI workload automation with AIPub.

Struggling to design AI infrastructure? Learn how reference architecture helps you choose the right GPU, optimize performance, and reduce risk with data-driven decisions.

Before buying more GPUs, check your AI infrastructure. Learn 5 essential steps to optimize GPU resources and improve performance with smarter operations.

More GPUs don't guarantee faster training — the network fabric may be the bottleneck. Here's why scaling efficiency drops and what to check first.

NVIDIA is shifting from selling GPUs to selling AI factory operating software. Here's what DSX, Tokens per Watt, and this shift mean for your GPU infrastructure.

Disaggregated Inference splits LLM inference's Prefill and Decode stages across separate GPUs — here's why, its performance benefits, and what to check before adopting it.

Learn how to manage hybrid and multi-cluster AI infrastructure. Discover unified orchestration strategies to optimize GPU utilization and reduce operational complexity.

Learn why GPU monitoring is critical for AI infrastructure. Discover how AI Pub enables real-time visibility, bottleneck detection, and cost optimization.

NVIDIA AI Factory redefines data centers as intelligence production systems. Learn how Blackwell NVL72, reasoning AI, and token economics are reshaping AI infrastructure.

What does NVIDIA mean by Extreme Co-Design? Explore how AI infrastructure is shifting from chip-level scaling to rack-level system design in the AI factory era.

At the 2026 World Cup, Football AI Pro reshapes tactical analysis and broadcasting — revealing why AI orchestration is essential for enterprise AI agents.

Learn what MLOps is and why it matters. From data to deployment and monitoring, explore the full machine learning lifecycle and how to run AI systems efficiently.

DeepSeek proved software can beat hardware limits. See how NVIDIA's Extreme Co-Design turns chips, networks, and systems into one AI factory.

LLM training across thousands of GPUs makes failures inevitable. This guide compares synchronous, asynchronous, and in-memory checkpointing strategies.