{"id":1109,"date":"2026-10-26T09:30:00","date_gmt":"2026-10-25T22:30:00","guid":{"rendered":"https:\/\/catharsis.net.au\/blog\/private-ai-lab-lessons-and-whats-next\/"},"modified":"2026-11-03T20:53:10","modified_gmt":"2026-11-03T09:53:10","slug":"private-ai-lab-lessons-and-whats-next","status":"publish","type":"post","link":"https:\/\/catharsis.net.au\/blog\/private-ai-lab-lessons-and-whats-next\/","title":{"rendered":"Is Private AI Worth It? Lessons From Seven Weeks in Our Lab (Part 11 of 11)"},"content":{"rendered":"\n

The last post in the series: what our private AI lab set out to prove, the platform we ended up with, ten lessons that started as real failures, where one local server falls short, and a checklist for planning your own.<\/p>\n\n\n\n

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Where this fits \u00b7 Running Local AI in Production \u00b7 Part 11 of 11<\/strong><\/p>\n\n\n\n

You are reading Part 11 of 11 in a series where we built a private AI server in our lab and ran realistic test workloads on it for seven weeks. Each part stands on its own. The previous part covered document, email and knowledge-base workflows that keep data on the server; this one covers the lessons from seven weeks, the honest limits and what comes next.<\/p>\n\n\n\n

\u2190 Part 10: Can AI Automate Business Workflows Without Sending Data Out?<\/a>  \u00b7  Series guide<\/a><\/p>\n\n\n\n

Just want it working? Follow the step-by-step setup guide<\/a>: local AI with Ollama and LiteLLM, from a bare server to a secure gateway.<\/p>\n\n\n\n

See all 11 parts of the series<\/summary>\n\n
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  1. Local AI or Cloud AI? Why We Built a Private AI Server<\/a>: why we built the lab on our own hardware, what ran on it and what that choice cost<\/li>\n\n\n
  2. How Do You Control Access to Local AI Models? Use a Gateway<\/a>: one LLM gateway as the only way to reach a model: keys, budgets and tracing<\/li>\n\n\n
  3. Why Does a Local AI Server Freeze? Sizing Memory for LLMs<\/a>: five hard freezes and the memory budget that ended them<\/li>\n\n\n
  4. What Happens When a Prompt Is Too Big for a Local LLM?<\/a>: fitting every prompt to a local model’s context window<\/li>\n\n\n
  5. How Do You Run Several AI Models on One GPU Without Slowdowns?<\/a>: routing requests across models on a single GPU without constant model swaps<\/li>\n\n\n
  6. How Do You Keep an AI Server Stable? GitOps for Local AI<\/a>: running the server from a Git repository: deploys, drift, pinning and safe AI agents<\/li>\n\n\n
  7. What Is Your Local AI Server Actually Doing? Observability Basics<\/a>: making the server observable: traces, dashboards, usage and heat<\/li>\n\n\n
  8. Can Local AI Agents Analyse Stocks? How We Tested and Scored Them<\/a>: a multi-agent stock-analysis experiment and how we scored the agents<\/li>\n\n\n
  9. Why Was Our Telegram AI Agent So Slow? The Hidden Prompt Cost<\/a>: a Telegram AI agent on local models, and why its fixed prompt became the budget<\/li>\n\n\n
  10. Can AI Automate Business Workflows Without Sending Data Out?<\/a>: document, email and knowledge-base workflows that keep data on the server<\/li>\n\n\n
  11. Is Private AI Worth It? Lessons From Seven Weeks in Our Lab<\/strong> (you are here): the lessons from seven weeks, the honest limits and what comes next<\/li>\n\n<\/ol>\n\n<\/details>\n\n<\/div><\/div>\n\n\n\n

    This series documents a private AI server we built and stress-tested in our lab. Workloads and data are synthetic or generalised; the problems, measurements and fixes are real.<\/em><\/p>\n\n\n\n

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    TL;DR<\/h3>\n\n\n\n