Ollama Runs Local Models. They Outperform Expectations. Don't Applause, It's Just Compression.
Recent releases of Gemma 4 and Qwen have made local LLMs surprisingly capable on consumer hardware. Simon Willison noted at PyCon US 2026 that laptop-available models now wildly outperform expectations. Ollama remains the easiest setup path, with native support in Visual Studio Code, JetBrains AI Assistant, Zed, and Posit Assistant.
The underlying principle here is capability compression. Smaller models trained on better data distributions now approach the utility of models ten times their size. The mechanism is mixture of experts architecture, where only relevant parameter subsets activate per query. You should understand that the frontier is not the only useful tier anymore.
Open-source developer Simon Willison documented the performance gains at PyCon US 2026. Google's own internal coding benchmarks show the smaller Gemma 4 variant performs close to its 26B MoE flagship version.
- Download Ollama from ollama.com and install it. Open your terminal and type 'ollama run gemma3:4b' to pull and start a model. Expected outcome: a local chat session begins in your terminal.
- In the same terminal, type 'Write a one-paragraph summary of photosynthesis' and press Enter. Expected outcome: the model generates a coherent response without any internet connection.
- Install the Continue extension in Visual Studio Code, point it at your local Ollama instance, and open a code file. Expected outcome: the extension connects to Ollama and you can ask it questions about your code locally.