Production Deployment Guide

Deploy NexusAI models to production environments with enterprise-grade reliability, auto-scaling, GPU optimization, and comprehensive observability.

Overview

Production deployment of NexusAI requires careful configuration of inference endpoints, resource allocation, and monitoring pipelines. This guide covers containerized deployments, cloud-native patterns, and serverless architectures optimized for low-latency AI inference.

ℹ️

Recommended Architecture

For production workloads, we recommend Kubernetes with Horizontal Pod Autoscaler (HPA) backed by GPU nodes, or managed services like AWS SageMaker / GCP Vertex AI for rapid deployment.

Prerequisites

Docker Engine ≥ 24.0 or containerd
Kubernetes 1.28+ (for cluster deployments)
NVIDIA CUDA 12.2+ drivers (for GPU acceleration)
NexusAI API credentials & organization ID
Minimum 16GB RAM, 8 vCPU (CPU-only) or 1x NVIDIA A10/V100 (GPU)

Docker & Kubernetes

Docker Compose

docker-compose.yml
version: '3.9'
services:
  nexus-inference:
    image: registry.nexus.ai/nexus-core:v3.2.1
    ports:
      - "8000:8000"
    environment:
      - NEXUS_API_KEY=${NEXUS_API_KEY}
      - MODEL_ID=nexus-v3-ultra
      - DEVICE=gpu
      - MAX_BATCH_SIZE=32
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all

Kubernetes Deployment

k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nexus-ai-inference
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nexus-inference
  template:
    metadata:
      labels:
        app: nexus-inference
    spec:
      containers:
      - name: nexus
        image: registry.nexus.ai/nexus-core:v3.2.1
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 16Gi
          requests:
            cpu: 2
            memory: 8Gi

Cloud Providers

NexusAI provides official Terraform modules and CLI tools for rapid provisioning across major cloud platforms.

Provider	Service	Command	Best For
AWS	SageMaker / EKS	`nexus deploy aws --region us-east-1`	Enterprise scale, multi-region
GCP	Vertex AI / GKE	`nexus deploy gcp --zone us-central1-a`	ML pipelines, TPUs
Azure	ML Studio / AKS	`nexus deploy azure --location eastus`	Microsoft ecosystem, compliance

Configuration & Environment Variables

All runtime behavior is controlled via environment variables or a config.yaml file.

Variable	Type	Default	Description
`NEXUS_API_KEY`	string	-	Authentication token for model registry
`MAX_BATCH_SIZE`	int	16	Max concurrent requests per worker
`QUANTIZATION`	string	none	fp16, int8, int4, or none
`ENABLE_TRACING`	bool	false	Enable OpenTelemetry distributed tracing

Scaling & Load Balancing

Horizontal Pod Autoscaler (HPA)

hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: nexus-ai-inference
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: nexus_queue_depth
      target:
        type: AverageValue
        averageValue: 50

⚠️

GPU Memory Limits

When auto-scaling GPU pods, ensure your node pool has sufficient VRAM. Use memoryFraction and cudaVisibleDevices to prevent OOM kills under load.

Monitoring & Logging

NexusAI exposes Prometheus-compatible metrics on /metrics by default. Key metrics include:

nexus_inference_latency_seconds - P50, P95, P99 response times
nexus_requests_total - Successful/failed request counters
nexus_gpu_memory_allocated_bytes - VRAM utilization
nexus_model_loading_time - Cold start duration

For production observability, deploy the official NexusAI Grafana dashboard (#18492) alongside Prometheus & Loki.

Security & Compliance

All endpoints support mTLS & OIDC authentication
Model weights are encrypted at rest using AES-256-GCM
Compliant with SOC 2 Type II, HIPAA, and GDPR by design
Enable REQUEST_SANDBOX=true to isolate untrusted prompts

Troubleshooting

High Latency or Timeout Errors

Check GPU utilization: nvidia-smi
Reduce MAX_BATCH_SIZE if VRAM is exhausted
Enable QUANTIZATION=fp16 to reduce memory footprint
Verify network policies aren't blocking inference traffic

🚨

Model Crashing on Startup

If pods fail with Exit Code 137, your requests/limits are misconfigured. Ensure resources.limits.memory matches your model size. Use kubectl describe pod to inspect OOM events.

FAQ

Can I deploy multiple models on a single instance?

Yes. Use the MODEL_REGISTRY environment variable to load multiple checkpoints. NexusAI handles dynamic routing and memory pooling automatically.

How do I perform zero-downtime updates?

Use Kubernetes Rolling Updates with maxUnavailable: 0 and preStop hooks to gracefully drain inference requests. NexusAI supports health check probes out of the box.

Is cold start mitigation available?

Yes. Enable WARM_POOL_SIZE=2 to maintain pre-loaded model instances. Combined with predictive scaling, cold starts are reduced to <100ms.

✅

Ready to deploy?

Run nexus init --template production to scaffold a complete deployment manifest with CI/CD pipelines, monitoring, and security policies pre-configured.