… This matters more for local models than cloud models Local AI needs context more than cloud models One thing I noticed while using local AI is that tool calling feels much more important than it does with cloud-based AI services. …
… Giving the model a local memory turned it from a generic AI assistant into a highly personalized engine that understands exactly how I operate. Related I turned my home server into an AI appliance, and this is the stack that actually stuck My reliable, low-friction self-hosted AI productivity setup. …
… My AI setup slowly turned into a model graveyard I was downloading models faster than I could actually use them When I first started experimenting with local AI models , I wanted to try almost everything. …
… Throughout my testing, I used the model the same way I use cloud-based AI tools. I asked it to brainstorm blog ideas, summarize articles, explain technical concepts, rewrite paragraphs, and answer general questions. …
… But after building out my local AI setup , I realized that model quality is only part of the equation . Speed, responsiveness, and how a model fits into my daily workflow matter just as much. A model can be more capable on paper and still be the wrong choice for everyday use. …
… I still use cloud models It’s not a competition, just an addition The framing that local LLM content loves is " replace your cloud AI ," and I get why it’s appealing as a pitch - I’ve definitely framed it that way myself. However, realistically, cloud AI isn’t going anywhere for me. …
… That's a trainable skill, not a function of scale, and the models that get it right are the ones that were trained for tool calls. The models that work, at every scale From 2.3B to 123B, the pattern holds For running agents locally in mid-2026, the Qwen family is the default for a reason. …
… I definitely didn't get it with local AI in mind because that wasn't even on my radar. It runs local LLMs fine. I can push a 20B model with GPU offloading without it falling apart, and the model I actually use day-to-day is Qwen 3.5 9B , which I can run at a 60k context window in my runner. …
… For example, when I tested the Arc A770 for local LLMs, I used ipex-llm through a custom Docker setup. It worked, and the performance was decent too, netting approximately 23 tokens per second on Qwen3's 14B parameter model. Having said that, getting there was painful. …
… As open models continue to get better, it has become more likely than ever to find a model that can replace, or at the very least, complement your cloud AI subscriptions. Ollama Ollama is a platform to download and run various open-source large language models LLM on your local computer. …