Local Interactive Simulation
Hyperion Inference Control Plane
Explore how queue pressure, workload intent, batching policy, and worker capacity can produce a transparent scheduling recommendation—without presenting simulated values as measured performance.
View GitHubServing Policy
Browser-only simulationScheduler Decision
Choose a workload shape to inspect Hyperion's illustrative scheduling policy.
No model, GPU, Firebase service, benchmark endpoint, or external API is called.
Validation Boundary
Substantial implementation, unfinished verification.
Hyperion independently implements serving, caching, batching, deployment, and observability concerns. It is locally tested as a reference implementation, not operated as a production inference service.
The current audit also finds 7 failures, primarily in cache integration and batching behavior.
Latency and throughput depend on hardware, model, runtime, and workload; reproducible results are still future work.
Repository Evidence
FastAPI endpoints, model lifecycle, CPU fallback, and GPU-aware configuration.
Request batching, response caching, and configurable queue behavior.
Docker, Helm, HPA, VPA, and KEDA configuration examples.
Prometheus, Grafana, tracing, structured logs, and alert definitions.