I trimmed down my local AI stack, and I should have done it sooner

Published Jul 21, 2026, 8:30 AM EDT Beginning his professional journey in the tech industry in 2018, Yash spent over three years as a Software Engineer. After that, he shifted his focus to empowering readers through informative and engaging content on his tech blog – DiGiTAL BiRYANi. He has also published tech articles for MakeTechEasier. He loves to explore new tech gadgets and platforms. When he is not writing, you’ll find him exploring food. He is known as Digital Chef Yash among his readers because of his love for Technology and Food. If you spend enough time exploring self-hosted AI, it's easy to believe that more tools mean a better setup. I thought the same and kept experimenting with every promising project that came along. Over time, though, I realized that my growing collection of AI tools wasn't making me more productive; it was making my workflow harder to manage. The real value of a local AI setup isn't how many models or applications you can run, but how well they fit into your daily work. Simplifying my stack completely changed that, and I only wish I had done it much sooner. I used to add everything to my local AI stack without removing anything My stack only ever grew When I first got into local AI, I treated every new project like it deserved a place in my setup. If someone recommended a new chat interface, agent framework, workflow builder, or model manager, I'd install it "just to try it." The problem was that I rarely removed anything afterward. My Docker dashboard slowly filled with containers, I had multiple web interfaces doing almost the same thing, and my SSD kept losing space to models I barely touched. At the time, it felt like I was building the ultimate local AI environment. Looking back, I was mostly collecting tools instead of building a workflow. Maintaining everything became a task on its own, with constant updates, occasional broken containers, and too many choices whenever I wanted to get something done. The more my stack grew, the less efficient it became. I had plenty of powerful tools, but I was spending more time managing them than actually using AI for the work that mattered. I stopped asking what was cool and started asking what I actually used I paid attention to my daily habits Instead of looking for the next interesting AI project, I started paying attention to the tools I naturally use every day. That simple shift changed how I looked at my entire setup. I noticed that I kept returning to the same few applications while many others sat idle for weeks. Some tools were technically impressive, but they never became part of my workflow. Others offered features I thought I'd need someday but never actually used. I also realized I had several tools solving the same problem, which only made choosing between them more difficult. So I stopped judging software by GitHub stars, Reddit recommendations, or YouTube demos. If a tool consistently helped me write, research, summarize documents, or test models, it stayed. If I had to remind myself it existed, it was probably time to remove it. That mindset made every decision much easier and helped me build a stack around my work instead of around whatever happened to be popular that week. The simple rule to trim down the AI stack The biggest improvement wasn't speed, it was simplicity Once I knew which tools I genuinely relied on, I made myself follow one simple rule: every tool had to serve a clear purpose. If two applications did the same job, I kept the one that fit my workflow better and removed the other. I followed the same approach with AI models. I didn't need five different general-purpose models when I always preferred the same one for everyday tasks. The goal wasn't to have the smallest setup possible; it was to avoid unnecessary overlap. As I removed duplicate tools and unused models, everything became easier to manage. Updates took less time, troubleshooting became less frequent, and I stopped wasting storage on software I wasn't opening. What surprised me most was that I didn't feel as though I had lost anything. My local AI setup still handled everything I needed, but it required far less attention. Simplicity turned out to be a much bigger productivity boost than adding another tool ever was. My current local AI stack It's built around the tools I already use every day My local AI stack is much smaller now, but it's far more useful because every part of it has a purpose. I use Ollama to run my local models and Open WebUI as my primary interface. Instead of keeping dozens of models, I've settled on three that cover almost everything I do: deepseek-r1:14b for everyday writing, research, and document analysis, gpt-oss:20b when I need higher-quality reasoning or more nuanced responses, and Qwen 2.5 Coder whenever I'm working with code. Beyond the models, AI is integrated into the tools I already use every day. I use it with Logseq to brainstorm ideas, Obsidian to summarize research and refine drafts, Paperless-ngx to analyze PDFs and documents, and Home Assistant for a few useful automations. That integration has made a much bigger difference than constantly trying new AI projects. My stack now feels intentional, reliable, and built around my workflow instead of around whatever tool or model is trending online. Less is more Cutting down my local AI stack reminded me that productivity isn't about having the most tools; it's about having the right ones. I still enjoy trying new projects, but they now have to earn a place in my workflow instead of staying installed by default. My setup is easier to maintain, easier to trust, and helps me get work done without unnecessary distractions. Looking back, simplifying my stack is one of the best changes I've made to my local AI setup.