Infrastructure
AI runs on physical infrastructure: GPUs, data centers, networking, power and the cloud platforms that tie them together. This section covers the hardware and systems behind the models — from NVIDIA's latest accelerators and "AI factories" to the cloud commitments and energy questions that increasingly set the pace of progress.
We break down chip launches, data‑center build‑outs, cloud partnerships and the supply‑and‑power constraints that decide how fast AI can scale. The goal is to make the stack legible: why a new GPU generation matters, what an inference cluster costs to run, and how infrastructure choices ripple up into the products people use.
For engineers, infrastructure teams and anyone curious about the engine room of modern AI, this is the place. Below you'll find the latest AI infrastructure news and explainers.
Best VPS for AI Agents in 2026: What to Look For
Running an AI agent 24/7 comes down to where you host it. What to look for in a VPS for AI agents in 2026 - uptime, root access, RAM, NVMe, location, support - plus why the model API is a separate cost, and Verpex as a pick that fits the checklist.
Why AI Hallucinations Still Happen
AI models still produce confident, wrong answers. The causes run deep: word prediction, uncertainty, thin data, weak prompts, and bad retrieval. Here is what actually drives hallucinations and how to keep them in check.
What Is a Context Window?
A context window is all the text an AI can consider at once. More room can sharpen answers, but it raises cost, slows responses, exposes more data, and can even hurt accuracy. Here is how to think about it.
What Are Tokens in AI Pricing?
AI is billed per token, not per question. This explains input vs output tokens, why long prompts and conversations cost more, and how caching cuts the bill — without quoting prices that vary by vendor.
How to Choose the Right AI Model for Each Task
Using one expensive model for everything wastes money and speed. A practical framework for matching tasks to small, medium, large, and premium model tiers.
Local AI vs Cloud AI: Why Hybrid Setups Are Becoming the Practical Middle Ground
Local and cloud AI trade off on cost, privacy, latency, flexibility, and operations. A non-ideological comparison of why hybrid setups have become the practical default.
What Is RAG and Why Do Chatbots Need It?
Plain AI chatbots answer from memory and guess about your business. RAG lets a bot look up your real documents first — here is how the retrieve-augment-generate loop works.
NVIDIA Vera Rubin in Production: AI Factories, 10x Per-Watt Inference
NVIDIA used its GTC Taipei and Computex stage to declare its next-generation Vera Rubin platform in full production and to push a single idea: the AI race is moving from chatbot windows into rack-scale "AI factories." The pitch targets agentic AI, reasoning, and long-context workloads — the workloads that run for minutes, not milliseconds. For anyone building, buying, or budgeting AI, this is the layer that decides cost per token. It is also a reminder that frontier AI now depends as much on power, cooling, and supply chains as on model weights.