Health Check

A health check is implemented as a dedicated, lightweight HTTP endpoint (e.g., /health or /ready) that returns a simple status code. Orchestrators like Kubernetes or load balancers periodically call this endpoint.

• A 200 OK response signals the instance is healthy.
• Any other response (e.g., 503 Service Unavailable) triggers a remediation action, such as restarting the pod. For inference servers, this probe must verify the base model is loaded, the GPU is accessible, and any required adapter modules are available in memory.

Two distinct types of health checks are used in container orchestration:

• Liveness Probe: Determines if the container is running. Failure results in the container being killed and restarted. This catches a process that is stuck in a deadlock.
• Readiness Probe: Determines if the container is ready to accept traffic. Failure removes the pod's IP from the service's load-balancing pool. This is used during model warm-up, adapter switching, or high load. For PEFT servers, a readiness probe may fail while a new LoRA module is being loaded, preventing requests from being routed to an unprepared instance.

A robust health check for an inference server must validate all critical dependencies, not just the main process. This includes:

• GPU/Accelerator Health: Verifying CUDA context and memory availability.
• Model Repository Connectivity: Ensuring access to storage (e.g., S3, model registry) for dynamic adapter fetching.
• External Services: Checking connections to feature stores, vector databases, or other microservices required for pre/post-processing. A failure in any dependency should cause the health check to fail, ensuring traffic is only sent to fully functional instances.

Advanced health checks can incorporate performance metrics to prevent routing traffic to degraded instances. This goes beyond simple binary "up/down" status.

• Latency Checks: The probe can perform a dummy inference and fail if response time exceeds a threshold (e.g., > 100ms), indicating potential GPU throttling or memory issues.
• Memory Pressure: Checking for excessive KV cache memory usage or GPU OOM conditions.
• Throughput Degradation: Monitoring the request queue length or batch processing rate. This proactive approach is essential for meeting Service Level Objectives (SLOs) for inference latency.

Health checks are configured declaratively in orchestration manifests (e.g., a Kubernetes Deployment). Key parameters define their behavior:

• initialDelaySeconds: Time to wait after startup before beginning probes.
• periodSeconds: How often to perform the probe.
• timeoutSeconds: Time to wait for a response.
• failureThreshold: Consecutive failures needed to declare an instance unhealthy. Proper tuning of these values is critical. For large model servers, a long initialDelaySeconds is required to account for cold start time during model loading.

The ultimate purpose of a health check is to trigger automated remediation to maintain service availability. Standard patterns include:

• Pod Restart: An unhealthy liveness probe causes the orchestrator to kill and recreate the container.
• Traffic Drainage: An unhealthy readiness probe removes the pod from the load balancer, allowing in-flight requests to complete before termination.
• Autoscaling Triggers: Health check failure rates can be used as a custom metric for the Horizontal Pod Autoscaler (HPA) to trigger scaling actions.
• Alerting: Integration with observability platforms like Prometheus and Grafana to alert engineers of persistent failures, which may indicate systemic issues like concept drift or corrupted model weights.

Core optimization techniques for maximizing GPU throughput during inference.

• Dynamic Batching: Groups multiple incoming requests into a single batch for parallel processing, forming batches based on arrival time.
• Continuous Batching (Iterative Batching): An advanced method for autoregressive models where new requests are added to a running batch as previous requests finish generation, leading to near-100% GPU utilization. This is essential for cost-effective LLM serving.

An inference architecture where a single base model instance can dynamically load and switch between multiple trained adapter modules or LoRA weights. This enables:

• Serving multiple tasks or tenants from one deployed model.
• Efficient resource use via adapter switching based on request metadata.
• Rapid deployment of new fine-tunes without restarting the entire service.

Safe deployment strategies for rolling out new model versions with minimal risk.

• Canary Deployment: The new version is released to a small, controlled subset of traffic first. Performance and stability are monitored before a full rollout.
• Shadow Mode: The new model processes live requests in parallel with the production model, but its predictions are only logged for comparison. This allows for validation without impacting users.

The practice of instrumenting systems to understand their internal state through external outputs. For PEFT servers, this involves collecting:

• Metrics: Latency, throughput, error rates, GPU utilization.
• Logs: Request/response payloads, adapter load events.
• Traces: Distributed tracing to follow a single request across microservices. This data is critical for debugging, performance tuning, and triggering autoscaling.

Mechanisms for managing compute resources efficiently in response to load.

• Autoscaling: Automatically adjusts the number of active instances (e.g., pods via Horizontal Pod Autoscaler) based on metrics like CPU or queue length.
• Cold Start: The latency penalty incurred when a new instance must load a model into memory from scratch. Mitigated by model warm-up procedures and predictive scaling.