What this error means
ollama pull [model] fails with 'Error: EOF' is a Ollama failure pattern reported for developers trying to fix ollama pull command failing with eof error when downloading large models. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
Active GitHub issue (May 2026) reports 'ollama pull gpt-oss-safeguard:120b' fails with 'Error: EOF' on aarch64 (NVIDIA DGX Spark/GB10). Reproduces on both 0.23.2 stable and 0.23.3-rc1. The 20 GB sibling model pulls normally, confirming the issue is specific to large model downloads.
Common causes
- Model downloads are the first step in using Ollama. When pull fails with EOF on specific large models (like gpt-oss-safeguard:120b), developers cannot access the models they need. The error is specific to model size and architecture, making it hard to diagnose.
- Active GitHub issue (May 2026) reports 'ollama pull gpt-oss-safeguard:120b' fails with 'Error: EOF' on aarch64 (NVIDIA DGX Spark/GB10). Reproduces on both 0.23.2 stable and 0.23.3-rc1. The 20 GB sibling model pulls normally, confirming the issue is specific to large model downloads.
Quick fixes
- Confirm the exact error signature matches
ollama pull [model] fails with 'Error: EOF'. - Check the Ollama account, local tool state, and provider configuration involved in the failing workflow.
- Verify the model name, local service connectivity, and network access before retrying the model pull.
Platform/tool-specific checks
- Verify the command, editor, extension, or API client that produced the error.
- Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
- Avoid deleting credentials, local model data, or project settings until the failing scope is clear.
Step-by-step troubleshooting
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- Keep the working fix documented for the team or deployment environment.
How to prevent it
- Keep provider/tool configuration documented.
- Record non-secret diagnostics such as tool version, provider name, model name, and command path.
- Add a lightweight check before CI or production workflows depend on the tool.