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AI inference vs. training: What they are and how they differ

AI inference and training are terms you'd run into if you have been around software engineering or even just scrolled through the news. Both are integral to delivering the AI-powered experiences we have come to expect from many of the applications we use daily. According to McKinsey, by 2030 inference will overtake training as the dominant workload in AI data centers, making up more than half of all AI compute and roughly 30-40% of total data center demand.

21 AI concepts every beginner should know before their first interview

If you’re prepping for your first AI or MLOps interview, the hardest part usually isn’t always the hands-on element. For me, it’s the vocabulary. Interviewers sometimes lob single-word concepts at you (“what’s quantization?”) and watch how far you can carry the thread. The questions sound clear-cut, but each one is really a doorway into a bigger topic, and the interviewer is judging how cleanly you walk through it.

Blackwell sold out in weeks. Here's what Rubin demand will look like.

"Blackwell sales are off the charts, and cloud GPUs are sold out. Compute demand keeps accelerating and compounding across training and inference, each growing exponentially. We've entered the virtuous cycle of AI." Jensen Huang, CEO, NVIDIA When NVIDIA's CEO makes that statement in a quarterly earnings release, it is not marketing language.

Understanding GPU cloud instance types: How to read a spec sheet for real-world ML performance

A GPU spec sheet is a confidence trick. It looks like an objective document - numbers, units, comparable rows - but most of the numbers on it don't map cleanly to the performance a real workload will see. Teams that pick GPUs by reading the headline figures usually find out the gap between spec and reality somewhere around the first production run. This is a working guide to reading GPU cloud instance specifications against actual ML workloads. The goal isn't to recommend a card.

Konstruct product updates: Global resources, MCP support, and smarter permissions

May has been one of our busiest months yet for Konstruct. Across three releases, 0.5, 0.5.1, and 0.5.2, we've shipped some of the most requested platform-level changes since we launched: a unified model for sharing resources across organizations, native support for AI-driven workflows via MCP, a completely redesigned API keys experience, and a cleanup to how permissions actually work in multi-org environments. Let's walk through what shipped and why it matters.

Secure execution: Agents in sandboxes with relaxAI

The hard part of deploying AI agents isn't the agent. It's the environment around it. As organisations move from AI experimentation into production, the question isn't just what agents can do; it's whether you can trust the environment they run in. Sandboxed execution gives you both the autonomy and the guardrails, keeping agents isolated, auditable, and under your control.

Digital sovereignty: Who's in control?

Digital sovereignty isn't a marketing buzzword. It's about jurisdiction, accountability, and operational certainty and it starts with where your data is hosted and how it's processed. Civo's UK sovereign cloud delivers public cloud, private cloud, and AI services, all hosted and operated exclusively within the United Kingdom under UK legal authority with no exposure to foreign control.

Civo AI: Strategy over complexity

Most cloud providers think AI is just a hardware problem. They focus on the GPUs, the racks, and the raw compute, but they leave the strategy up to you. At Civo, we do AI differently. We don't just provide the hardware; we guide you through the full life cycle of AI adoption, from initial planning to scaling production workloads. By leveraging best-in-class NVIDIA models and GPUs, we give you the performance to unlock AI at scale without the fear of being bogged down by complexity. It's more than infrastructure, it’s cloud freedom with AI built-in.

Self-host AI on Kubernetes: GPU clusters, private models, and the GitOps Catalog

Spin up a GPU workload cluster using Konstruct's new GPU cluster templates, deploy a self-hosted LLM, and use it in your organization — all live on stream. This hands-on session shows how shipping AI workloads to GPU clusters is just as easy as deploying to Konstruct physical or virtual clusters, and how open source apps in the GitOps Catalog make it even faster. Walk away knowing how to cut your token spend by running models privately on your own infrastructure.