Guides
AI Infrastructure Guides
Practical answers to the questions founders and CTOs ask before they deploy. No fluff, no vendor spin.
Browse by Topic
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
GPU Colocation vs. Cloud for AI Startups
When a startup should stop renting cloud GPUs and colocate owned hardware instead — the timing, the tradeoffs, and how to make the switch without slowing the team down.
💰 EconomicsOn-Prem LLM Deployment vs. Cloud: The Real Cost Breakdown
What an LLM actually costs to run on owned hardware versus cloud GPUs — the full TCO, the break-even point, and the costs both sides forget to mention.
🖥️ Hardware 101What GPU Do I Need to Run a 70B Model?
How much VRAM a 70B-parameter LLM actually needs, how quantization and context length change the answer, and which GPU configurations serve it in production.
📍 Austin MarketThe Austin GPU Colocation Landscape in 2026
Where to put GPU hardware in Central Texas — the facilities, the power situation, the ecosystem, and what actually makes Austin a sensible base for AI infrastructure.
💰 EconomicsAMD's Agent Computer Pitch: Where It Holds Up
AMD wants you to buy a desktop, run AI locally, and stop paying per token. Here's the real math — and where it fits beside a colocation rack.
🗺️ PlanningHow 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.