What this error means

Ollama MLX runner enters repetition loop at 60K+ tokens context, no abort mechanism, streams same phrase hundreds of times indefinitely is a Ollama failure pattern reported for developers trying to fix ollama mlx runner infinite repetition loop hang during large-context inference with no detection or circuit breaker. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #16179 on ollama/ollama (2026-05-15): On Apple Silicon MLX runner with 150K+ token contexts, models enter repetition loops outputting same phrase 50+ times. Ollama has no mechanism to detect or abort. Caused 30+ minute hang in agent framework. Workaround: switch to vllm-mlx. Category: Ollama (local LLM serving, paid hardware investment).

Common causes

  • GitHub issue #16179 on ollama/ollama (2026-05-15): On Apple Silicon MLX runner with 150K+ token contexts, models enter repetition loops outputting same phrase 50+ times. Ollama has no mechanism to detect or abort. Caused 30+ minute hang in agent framework. Workaround: switch to vllm-mlx. Category: Ollama (local LLM serving, paid hardware investment).

Quick fixes

  1. Confirm the exact error signature matches Ollama MLX runner enters repetition loop at 60K+ tokens context, no abort mechanism, streams same phrase hundreds of times indefinitely.
  2. Check the Ollama account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

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.