Guides
AI Infrastructure Guides
Practical answers to the questions founders and CTOs ask before they deploy. No fluff, no vendor spin.
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Hardware 101
GPU servers, racks, and AI infrastructure fundamentals explained plainly.
Browse Hardware 101 →Planning
How to define requirements, size hardware, and build a deployment roadmap.
Browse Planning →Colocation
On-prem vs. cloud vs. colo — the real tradeoffs for AI infrastructure.
Browse Colocation →Economics
TCO models, cloud cost comparisons, and when the math favors owned hardware.
Browse Economics →Austin Market
AI infrastructure opportunities, facilities, and the local ecosystem in Austin, TX.
Browse Austin Market →Recent Articles
How to Plan Your First GPU Deployment
The questions you need to answer before buying a single GPU — workloads, scale, facility requirements, and the mistakes first-timers always make.
🏗️ ColocationOn-Prem vs. Colocation vs. Cloud for AI Workloads
Three options for running AI inference at scale. Here's how to think through the tradeoffs — power, cost, control, and who manages what.
🖥️ Hardware 101What Is an AI Inference Rack?
If you're deploying an LLM or diffusion model in production, you'll hear the term 'inference rack' constantly. Here's what it actually means — in plain language.