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

  1. Confirm the exact error signature matches ollama pull [model] fails with 'Error: EOF'.
  2. Check the Ollama account, local tool state, and provider configuration involved in the failing workflow.
  3. 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

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. 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.