LiteLLM Ghost Models with Multiple Workers — Redis Pub/Sub Cache Invalidation Failure

What this error means

Deleted models persist in other workers' local cache when running LiteLLM with --num_workers > 1 is a LiteLLM failure pattern reported for developers trying to fix litellm proxy where deleted models remain available on some workers after deletion via /model/delete api. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

LiteLLM issue #27852 reports that with —num_workers 4 and Redis cache, deleting a model added via /model/new API does not invalidate the local in-memory cache of other workers. Ghost models continue serving requests, creating inconsistent API behavior.

Common causes

• Multi-worker LiteLLM deployments using Redis cache experience ghost models — models deleted via API continue to serve requests on some workers because Redis Pub/Sub doesn’t invalidate local in-memory caches across all workers. This causes inconsistent behavior and stale model serving.
• LiteLLM issue #27852 reports that with —num_workers 4 and Redis cache, deleting a model added via /model/new API does not invalidate the local in-memory cache of other workers. Ghost models continue serving requests, creating inconsistent API behavior.

Quick fixes

1. Confirm the exact error signature matches Deleted models persist in other workers' local cache when running LiteLLM with --num_workers > 1.
2. Check the LiteLLM 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.

Sources checked

• https://github.com/BerriAI/litellm/issues/27852

Evidence note: LiteLLM issue #27852 reports that with —num_workers 4 and Redis cache, deleting a model added via /model/new API does not invalidate the local in-memory cache of other workers. Ghost models continue serving requests, creating inconsistent API behavior.

Related errors

• LiteLLM model routing fails after hot reload
• LiteLLM Redis cache key collision

FAQ

What should I check first?

Start with the exact Deleted models persist in other workers' local cache when running LiteLLM with --num_workers > 1 text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed LiteLLM workflow until the root cause is understood.

Is this guaranteed to have one fix?

No. The imported evidence supports the troubleshooting path above, but tool behavior can vary by account, plan, version, provider, and local configuration.

How do I know the fix worked?

Rerun the same command, editor action, or request. The fix is working when that action completes without Deleted models persist in other workers' local cache when running LiteLLM with --num_workers > 1.