LiteLLM / LiteLLM
LiteLLM IP Usage-Based Dynamic Priority Queuing (Fair Queuing)
Implement fair queuing in LiteLLM proxy to prevent single client from monopolizing model capacity and causing starvation for other tenants Includes evidence for LiteLLM troubleshooting demand.
- Category
- LiteLLM
- Error signature
No fair queuing — traffic burst from one client starves other clients' requests- Quick fix
- Compare the failing environment with a known working setup, then change one configuration value at a time.
- Updated
What this error means
No fair queuing — traffic burst from one client starves other clients' requests is a LiteLLM failure pattern reported for developers trying to implement fair queuing in litellm proxy to prevent single client from monopolizing model capacity and causing starvation for other tenants. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
Feature request #18128 in BerriAI/litellm proposes IP-based dynamic priority queuing (fair queuing) for LiteLLM proxy. Without this, a single IP/client can flood the proxy and cause request starvation for other tenants. Strong commercial value for multi-tenant LLM proxy deployments.
Common causes
- Feature request #18128 in BerriAI/litellm proposes IP-based dynamic priority queuing (fair queuing) for LiteLLM proxy. Without this, a single IP/client can flood the proxy and cause request starvation for other tenants. Strong commercial value for multi-tenant LLM proxy deployments.
Quick fixes
- Confirm the exact error signature matches
No fair queuing — traffic burst from one client starves other clients' requests. - Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
- 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
- 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.
Sources checked
Evidence note: Feature request #18128 in BerriAI/litellm proposes IP-based dynamic priority queuing (fair queuing) for LiteLLM proxy. Without this, a single IP/client can flood the proxy and cause request starvation for other tenants. Strong commercial value for multi-tenant LLM proxy deployments.
Related errors
- LiteLLM
FAQ
What should I check first?
Start with the exact No fair queuing — traffic burst from one client starves other clients' requests 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 No fair queuing — traffic burst from one client starves other clients' requests.