> For clean Markdown content of this page, append .md to this URL. For the complete documentation index, see https://docs.nvidia.com/dynamo/llms.txt. For full content including API reference and SDK examples, see https://docs.nvidia.com/dynamo/llms-full.txt.

# Operations

## Docs

- [Observability](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/observability.md): View Dynamo metrics, configure structured logs and distributed traces, capture scheduler telemetry, and check deployment health
- [Snapshotting GPU Workers](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/cold-start-optimizations/dynamo-snapshot.md): Checkpoints initialized GPU workers with CRIU and cuda-checkpoint so later pods warm-start in seconds instead of minutes.
- [ModelExpress](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/cold-start-optimizations/model-express.md): Speed up model weight distribution across Kubernetes workers
- [DynoSim](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/dynosim/overview.md): Simulate Dynamo deployment choices before spending GPU time
- [DynoSim Simulation Model](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/dynosim/simulation-model.md): Engine behavior, timing sources, KV movement, and fidelity boundaries
- [Simulate a Kubernetes Deployment with Mocker](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/dynosim/live-simulation-with-mocker.md): Exercise the Dynamo frontend, router, and worker lifecycle without GPUs
- [Run a DynoSim Simulation](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/dynosim/simulation-runs.md): Replay a synthetic workload or saved trace against one simulated configuration
- [Sweep DynoSim Configurations](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/dynosim/simulation-sweeps.md): Search simulated topology, worker, and router choices before using GPU time
- [Benchmark Planner Decisions with DynoSim](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/dynosim/planner-simulation-benchmarking.md): Compare topology and scaling decisions against a saved trace
- [Benchmark a Kubernetes Deployment with AIPerf](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/benchmarking-with-ai-perf.md): Measure latency and throughput through a DynamoGraphDeployment frontend
- [Advanced Performance Tuning](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/performance-tuning.md): Measure a deployed configuration and tune the remaining workload-specific parameters
- [LoRA Adapters](https://docs.nvidia.com/dynamo/v1.4.1/kubernetes/operations/lo-ra-adapters.md): Serve fine-tuned LoRA adapters with dynamic loading and routing in Dynamo