Why and How to Run Local Models in Zed
Local models offer several advantages over cloud-hosted options, including privacy and cost-effectiveness. They allow users to maintain control over their data and avoid unexpected pricing changes. However, local models may not match the capabilities of frontier models available from top AI labs.
- ▪Local models provide absolute data privacy as they operate on the user's hardware.
- ▪The usage of local models in Zed has grown threefold in the last ten weeks.
- ▪While local models can be cheaper and more controllable, they may not perform as well as cloud-hosted frontier models.
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For many tasks, I prefer to use local models. When I need the best possible model, I still reach for frontier options, but a lot of the time I don't need that. I prefer something that runs on my machine, keeps my data on hardware I control, and won't disappear because a provider changed their pricing or limits. Open-weight models are getting better, too. Tools like LM Studio, Ollama, and llama.cpp keep getting easier to use, and in the last 10 weeks, local model usage has grown 3x in Zed's agent. At Zed, we're not building AI features for the money, and we're not in the business of locking devs into one way of using AI. We make it easy to use whatever provider you prefer, whether that's Codex over ACP, your own API key, or a direct subscription to Zed Pro.
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