What the Data for This Category Looks Like
Model allocation category data originates from two primary sources. First, large model metadata accessed by the platform, including model identifiers, call addresses, authentication keys, and other related information. Second, traffic routing rules and weight allocation schemes configured by users, alongside real-time collected call traffic statistics.
Data update rhythms fall into two categories. Routing rules are updated on demand. Traffic statistics data is synchronized in real time.
The data document structure follows a standardized format. Each entry includes four core fields: model ID, allocation weight, bound application node, and effective time. The weight field uses percentage units. The model ID uses a string format.
How These Data Characteristics Impact Deployment and Upgrade Processes
The data characteristics of the model allocation category impose multiple constraints on deployment and upgrade workflows. First, deployment relies on complete external large model metadata. Complete access verification must be completed for all models to be allocated before deployment. Otherwise, routing will point to invalid addresses. Second, the requirement to synchronize real-time traffic statistics means the data collection link must be retained during upgrades. An interrupted link will cause weight allocation deviations. Third, in multi-node deployment scenarios, the model allocation configuration of all nodes must stay consistent. Otherwise, cross-node traffic routing will be inconsistent. Fourth, the on-demand update feature of routing rules requires upgrades to support hot loading of configurations. Changes can take effect without restarting core services.
How to Set Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MODEL_ROUTE_STRATEGY | Weighted round-robin / Minimum latency | Choose based on business traffic characteristics. Weighted round-robin is suitable for fixed weight allocation scenarios. Minimum latency is suitable for dynamically adapting to model load fluctuations |
MODEL_ALLOCATE_WEIGHT_RANGE | 0-100 (percent) | The total weight must equal 100% to ensure all traffic is fully allocated and avoid unrouted requests |
PROXY_MODEL_TIMEOUT | 30-60 seconds | Matches the standard timeout window for large model calls, avoiding request failures caused by overly long wait times |
AIPROXY_MODEL_SYNC_INTERVAL | 5 minutes | Balances the timeliness of model metadata updates and system resource usage, avoiding additional load from frequent synchronization |
MODEL_FAILOVER_THRESHOLD | 3 consecutive failed calls | Filters out model nodes experiencing temporary failures, preventing continuous traffic allocation to unavailable model services |
GPU_STACK_MODEL_ENDPOINT | Fill in the actual deployed GPUStack API address | Ensures the request path matches the server configuration when connecting to privately deployed model services |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on one’s own samples before finalizing settings.
Three Common Configuration Errors
- After updating the aiproxy image, some nodes return 404 errors for model calls. Cause: The aiproxy image was not updated synchronously across all deployment nodes, leading to inconsistent routing configurations between nodes.
- After connecting the M3E embedding model to aiproxy, calls return empty vector data. Cause: The vector generation interface path and corresponding parameters for M3E were not added to the aiproxy model configuration, causing requests to fail to match the model service correctly.
- After upgrading the open-source version, the identity authentication function for shared links fails. Cause: The newly added authentication configuration item in the new version was not enabled, or the authentication key was not configured correctly.
How to Verify a Correct Configuration
- View the aiproxy runtime logs to confirm all configured model services can return normal responses, with no error messages for connection timeouts or authentication failures.
- Test model allocation rules with different weights, and observe whether traffic is routed to the corresponding model services according to the preset proportions.
- Check the model allocation configuration files of all deployment nodes to ensure the content is completely consistent, with no missing or incorrect fields.
- After connecting to a privately deployed model service, call the test interface to confirm the returned results match those from direct calls to the model service.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.