Running Local LLMs With Ollama For Private Development

Dev.to / 6/16/2026

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Key Points

The article explains that Ollama is essentially a wrapper around llama.cpp, providing a simplified “Docker for LLMs” experience with an HTTP server and easy model pulls/runs.
It highlights a key local-dev pitfall: Ollama defaults to a 2048-token context window and silently truncates anything beyond it, which can cause the model to miss parts of your input without errors.
It describes the GGUF model format used by Ollama as a self-contained package that includes not only weights but also tokenizer configuration, architecture details, and hyperparameters like trained context length.
It emphasizes that whether a model runs well depends more on the memory footprint after quantization (not raw parameter count), since quantization reduces precision and lowers memory/bandwidth pressure during inference.
It frames the practical tradeoff of using local models versus calling an API, encouraging readers to understand what’s actually running on their machine before deciding.

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