Deploy AI Factory with Hybrid Manager UI

Deploy AI Factory with Hybrid Manager UI#

Overview#

This guide provides deployment procedures for AI Factory components through the Hybrid Manager (HCP) web interface. The deployment process covers GPU infrastructure setup, NVIDIA NIM model deployment, and Gen AI application configuration.

Prerequisites#

Infrastructure Requirements#

Before deploying AI Factory components:

  • Kubernetes cluster with GPU nodes configured

  • NVIDIA GPU operator installed

  • Access to NVIDIA NGC registry or private registry

  • Object storage for model profiles (air-gapped deployments)

GPU Node Configuration#

Verify GPU nodes meet NVIDIA NIM requirements:

Model Type

NIM Model

GPU Requirements

Container Image

Text Completion

llama-3.3-nemotron-super-49b-v1

4 x L40S

nvcr.io/nim/nvidia/llama-3.3-nemotron-super-49b-v1:1.8.5

Text Embeddings

llama-3.2-nemoretriever-300m-embed-v1

1 x L40S

nvcr.io/nim/nvidia/llama-3.2-nemoretriever-300m-embed-v1:latest

Image Embeddings

nvclip

1 x L40S

nvcr.io/nim/nvidia/nvclip:latest

OCR

paddleocr

1 x L40S

nvcr.io/nim/baidu/paddleocr:latest

Text Reranking

llama-3.2-nv-rerankqa-1b-v2

1 x L40S

nvcr.io/nim/nvidia/llama-3.2-nv-rerankqa-1b-v2:latest

Ensure GPU nodes include:

  • Label: nvidia.com/gpu=true

  • Taint: nvidia.com/gpu

Step 1: Configure Registry Authentication#

Internet-Connected Deployments#

For clusters with internet access, configure NVIDIA NGC authentication:

1. Obtain NGC API key from NVIDIA NGC Portal

  1. Create authentication secrets via HCP UI:

  • Navigate to Project Settings → Secrets

  • Create nvidia-nim-secrets with NGC_API_KEY

  • Create ngc-cred Docker registry secret

Alternatively, use kubectl:

NGC_API_KEY=<your-ngc-api-key>

#  Create runtime secret

kubectl -n default create secret generic nvidia-nim-secrets \
    --from-literal=NGC_API_KEY=${NGC_API_KEY}

kubectl -n default annotate secret nvidia-nim-secrets \
    replicator.v1.mittwald.de/replicate-to=m-.*

#  Create image pull secret

kubectl -n default create secret docker-registry ngc-cred \
    --docker-server=nvcr.io \
    --docker-username=$oauthtoken \
    --docker-password=${NGC_API_KEY}

kubectl -n default annotate secret ngc-cred \
    replicator.v1.mittwald.de/replicate-to=m-.*

Air-Gapped Deployments#

For environments without internet access:

  1. Mirror NIM Images: Copy required images to private registry using skopeo

  2. Update Model URLs: Configure HCP to reference private registry locations

  3. Cache Profiles: Download and store model profiles in object storage

  4. Configure Storage Path: Reference cached profiles during model deployment

See Air-Gapped Configuration for detailed procedures.

Step 2: Access Model Library#

Navigate to the Model Library interface:

  1. Log into Hybrid Manager console

  2. Select your project from the project selector

  3. Navigate to AI Factory → Model Library

The Model Library displays available NVIDIA NIM models:

  • llama-3.3-nemotron-super-49b-v1: Advanced reasoning and chat capabilities (128K context)

  • llama-3.2-nemoretriever-300m-embed-v1: High-quality text embeddings

  • llama-3.2-nv-rerankqa-1b-v2: Multilingual query-document reranking

  • paddleocr: Ultra-lightweight OCR system

  • nvclip: Multimodal embeddings for image and text

Step 3: Deploy Model Server Cluster#

Select Model for Deployment#

  1. Click Deploy Model in Model Library

  2. Select target model from available options

  3. Review model requirements and documentation links

Configure Deployment Parameters#

Configure the model server cluster settings:

Instance Configuration

  • Server Instances: Default 1 (increase for high availability)

  • Minimum Instances: 1 (0 for scale-to-zero when supported)

  • Maximum Instances: Based on load requirements

Resource Allocation

  • Memory: Configure based on model requirements

  • Text completion models: 64-128 GB

  • Embedding models: 32-64 GB

  • CPU: Default values typically sufficient

  • GPU: Match documented requirements

  • llama-3.3-nemotron: 4 GPUs

  • Other models: 1 GPU typical

Scaling Configuration

  • Concurrent Request Threshold: Configure autoscaling trigger

  • Scale to Zero: Enable when supported (may be unavailable in initial release)

Deploy Model#

  1. Review configuration summary

  2. Click Deploy to initiate deployment

  3. Monitor deployment status in Model Server Clusters view

Deployment creates:

  • KServe InferenceService resource

  • Predictor pods with configured resources

  • Service endpoints for model access

Step 4: Monitor Deployment Status#

View Model Server Clusters#

Navigate to AI Factory → Model Server Clusters to view:

  • Cluster display name

  • Load balancer ingress URI

  • Deployment status (Active/Healthy, Pending, Failed)

  • Model details and tags

  • Resource utilization metrics

Access Cluster Details#

Click on a cluster name to view:

  • Detailed configuration parameters

  • Real-time health metrics

  • Grafana dashboard integration

  • Inference latency charts

  • System health indicators

Step 5: Configure API Access#

Generate API Tokens#

For external access to deployed models:

  1. Navigate to AI Factory → API Token Management

  2. Click Create Token

  3. Provide token reference name

  4. Store generated token securely

API tokens enable:

  • RAG application authentication

  • External service integration

  • Programmatic model access

Access Endpoints#

Models expose OpenAI-compatible endpoints:

Internal Access (within cluster):

http://<service-name>.<namespace>.svc.cluster.local/v1/chat/completions

External Access (with ingress):

https://<ingress-url>/v1/chat/completions

Step 6: Build Gen AI Applications#

Create Knowledge Base#

  1. Navigate to AI Factory → Gen AI Builder → Knowledge Bases

  2. Configure data sources:

  • PostgreSQL databases

  • Document repositories

  • API connections

  1. Select embedding model from deployed services

  2. Process content to generate embeddings

Configure Assistant#

  1. Navigate to AI Factory → Assistants

  2. Create new assistant with:

  • Name and directive

  • Model endpoint selection

  • Knowledge base integration

  • Tool configuration

  1. Deploy assistant application

See Create Assistant Guide for detailed procedures.

Step 7: Update and Manage Models#

Edit Model Server Cluster#

To modify running clusters:

  1. Navigate to Model Server Clusters

  2. Select cluster to edit

  3. Adjust parameters:

  • Instance counts

  • Resource allocations

  • Scaling thresholds

  1. Apply changes (triggers rolling update)

Rolling Updates#

Updates maintain availability through:

  • Connection draining before restart

  • Health verification between instances

  • Automatic rollback on failure

Troubleshooting#

Common Deployment Issues#

Model Fails to Start

  • Verify GPU availability matches requirements

  • Check registry authentication secrets

  • Review pod events and logs

High Inference Latency

  • Adjust batch size parameters

  • Increase GPU allocation

  • Scale replicas for load distribution

API Token Issues

  • Verify token hasn’t expired

  • Check network policies

  • Confirm ingress configuration

For detailed diagnostics, see Troubleshooting AI Factory on Hybrid Manager .

Additional Resources#

Reference Documentation

NVIDIA Model Documentation

Contents