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Ollama released v0.40.2 on 9 October 2026. The release changes how previously downloaded models are handled, upgrading them in the background the first time they are run with the new version.
The upgrade is intended to improve performance and compatibility when models run on llama.cpp. Ollama also keeps the original model copy as a backup, so users can downgrade safely if they need to return to the earlier version.
For someone making things, the main benefit is a less disruptive upgrade path. Existing models do not need to be manually replaced before testing v0.40.2. Instead, Ollama performs the model upgrade when the model is first run. That reduces the amount of setup work involved when moving an existing local workflow to the new release.
The change matters most for creators who already have models downloaded and rely on repeatable local runs. A model that worked with an earlier Ollama version may need a compatible format or runner configuration for llama.cpp. The release notes do not list individual model families or specific performance results, but they identify better llama.cpp performance and compatibility as the purpose of the background upgrade.
There is a storage tradeoff. Because Ollama preserves the original copy, upgraded models temporarily occupy additional disk space. This backup allows a safer downgrade, but users with large model collections should check available storage before running many models for the first time after updating.
The release notes say that a future Ollama release will remove these backups automatically. For now, users can remove backed-up models manually. The cleanup method requires jq and uses Ollama’s local API to find model manifests associated with the llamacpp runner, identify older ggml entries, and remove the matching objects. That command should be used carefully, because it deletes backup data rather than simply hiding it.
The release is available on Mina Labs for 0 credits. That makes it possible to use the listed offering without treating the local model upgrade process as a separate purchasing decision. The practical value here is the combination of access and a release that handles model compatibility in the background.
We would use Ollama v0.40.2 for an existing local workflow where several downloaded models need to be tested against a newer llama.cpp-based runtime. We would update Ollama, run each model once, and allow the background conversion to complete before comparing results. We would also monitor disk usage during that process and keep the original backups until the updated models had been checked.
For a production or shared environment, we would test the release on a copy of the workflow first. The backup behavior makes rollback safer, but it does not remove the need to verify outputs, storage requirements, and any scripts that depend on the local Ollama API. After validation, the manual cleanup command could remove no-longer-needed backups until automatic cleanup arrives in a later release.