Inference Check-AI

predictable inference performance across customers.

Some of the features that took care of :-

Scheduling based on estimated work:- -Requests vary substantially: a short question and a long document-analysis request can consume very different amounts of computation and memory. -It estimates request cost using prompt length, the output limit, model configuration, current load, and measured performance profiles. It then uses weighted queues, per-customer concurrency limits, and deadline-aware admission.Start with conventional scheduling algorithms and a calibrated statistical estimator. Use queue aging or minimum allocations to keep lower-priority customers from starving.

GPU optimization tied to customer workloads:- -Compile and validate artifacts before deployment. Measure whether an operation-level improvement actually benefits the complete request. Emmy provides compilation, tuning, benchmarking, and serving integrations that can support this workflow.

Customer policies that become enforceable runtime limits -Each customer receives a policy covering allowed models, concurrent requests, input and output limits, queue deadlines, spending budgets, and permitted deployment regions.

Security through resource and placement controls -Security would be part of request admission and routing:

• Bound prompt sizes, output limits, concurrency, and retry rates.
• Prevent one customer from exhausting shared queue or inference capacity.
• Route sensitive workloads only to approved infrastructure.
• Restrict access to customer logs, credentials, and usage records.
• Require validated, versioned policies and deployment artifacts.

Built With

• grafana
• nebuis
• nvedia
• python
• rust
• typescript
• vllm