Ai

The AI budget conversation is the hot topic this year. So, it’s safe to say that If you've probably heard some version of this line: token costs are falling fast and AI is about to get a lot cheaper.  It's a reasonable proposition. You want to believe it. But according to Gartner's own research, it’s a notion that is mostly wrong for the people actually paying the bills. Gartner's forecast, published this spring, makes some other striking proclamations. By 2030, they believe that running infer

Let’s imagine that you’ve budgeted $10,000 for an AI/LLM training run. You hop on  AWS and the app says, “no H100s available for 72 hours”. Apart from some stress and frustration, what does that delay actually cost your team? In answering that question, perhaps like Thanos, when asked by Dr. Strange how much it cost to collect the infinity rings, he replied: “Everything”. All joking aside, most finance models would record “zero”. There’s no invoice because you’ve consumed no GPU-hours. Your $10

When you ask an AI to help with something and it refuses, that refusal didn't happen by accident. Someone, or more precisely, a team of researchers, lawyers, and ethicists at a major AI lab, made a deliberate choice to build that boundary into the model. Today, major model providers (e.g. OpenAI, Anthropic, Google, Meta, Mistral, and Cohere) each maintain their own alignment teams, each with distinct values, risk tolerances, and commercial pressures shaping what their models will and won't do. T

Cloud storage abstracts away the very urgent compliance question that any AI startup or LLM research project should be asking itself: Where does my data physically sit? The answer to this question has very real and direct consequences, including legal, financial, and operational. Under GDPR, processing EU personal data on US-based infrastructure without adequate safeguards exposes companies to fines up to €20 million or 4% of global annual turnover, whichever is higher. Beyond regulation, there

Your 2026 guide to building a purpose-built GPU cluster for AI. Includes TCO, vendor-agnostic benchmarks, hardware selection (H100/MI300X), and rollout plan.

Z.ai's GLM-4.7-Flash (30B MoE) is live on io.intelligence. Get the strongest 30B model for coding & reasoning with best-in-class performance-per-dollar.

Complete technical guide to decentralized compute: benchmarks, cost calculator, compliance checklist, and step-by-step migration from AWS/GCP.

GLM-4.7 is now live on io.intelligence. Z.ai's open-source coding model scores 84.9% on LiveCodeBench vs Claude's 64%. Access it via a single API endpoint.

Complete comparison of GPU vs CPU for AI: deep learning performance, hardware cost, TCO, and ideal use cases. Choose the right processor for your training and inference workloads.

Unified Chat is the single, intelligent AI workspace that unifies every model and tool. Auto-routes for optimal quality and cost. End fragmentation.

io.net surpasses $20M in verifiable on-chain revenue, proving decentralized GPU infrastructure can compete with AWS and GCP on cost, performance, and real-world adoption.

Discover how AI data centers optimize workloads, boost efficiency, and power the future of artificial intelligence with advanced infrastructure.