FinTech Onboarding Deployment Guide

FinTech Onboarding Deployment Guide#

Solution Blueprints are provided as Helm Charts. The recommended approach to deploy them is to pipe the output of helm template to kubectl apply -f -. We don’t recommend helm install, which by default uses a Secret to keep track of the related resources. This does not work well with Enterprise clusters that often have limitations on the kinds of resources that regular users are allowed to create.

This blueprint supports AMD Instinct (default) and AMD Radeon platforms. Unless otherwise specified, the commands below cover the default Instinct deployment. For deployment with Radeon, see:

Multi-platform Support#

The chart ships defaults for two platforms, selected with --set global.platform=<platform>: instinct (GPU, the default) and radeon (GPU). Each sets a matching AIM image and resource profile; inspect them with helm show values . --jsonpath '{.vlm.platformDefaults}'.

Helm note: Built and tested on Helm 3.17 or higher. On Helm v4, if the piped kubectl apply is rejected, run helm pull oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding --untar first and template the local ./aimsb-fintech-onboarding directory instead.

AMD Instinct (GPU, default)#

To deploy the blueprint, run the following command:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  | kubectl apply -f - -n $namespace

AMD Radeon (GPU)#

To deploy the blueprint, run the following command:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  --set global.platform=radeon \
  | kubectl apply -f - -n $namespace

GPU Support (AMD ROCm)#

The service requires at least 1 AMD GPU to run. If you don’t already have a VLM deployed, you’ll need an additional GPU, for a total of at least 2 GPUs for this deployment. All necessary GPU parameters are configured automatically.

Quick Start#

The sections below cover fintech-specific deployment options (with or without an existing VLM). For platform selection, see Multi-platform Support above.

There are two deployment options:

If you don’t have an existing VLM deployment#

Example command to start the service:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  | kubectl apply -f - -n $namespace

Wait until all pods are Ready, then port-forward to the UI with the following command:

kubectl port-forward svc/$name-aimsb-fintech-onboarding-ui 8080:8080 -n $namespace

To clean up:

helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  | kubectl delete -f - -n $namespace

If you already have a deployed VLM service#

Example command to start the service:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  --set vlm.existingService="129.212.190.161:8000" \
  | kubectl apply -f - -n $namespace

Important notes about the vlm.existingService parameter:

  • vlm.existingService is only the base address of the model service, without API path suffix /v1 and without http:// prefix. Correct examples:

    • 129.212.190.161:8000

    • mistralai-small-24B-Instruct

    • my-model.default.svc.cluster.local

    • vlm-fintech-onboarding.svc.cluster.local:8000

  • Do NOT add /v1/chat/completions, /api, /openai, etc. at the end.

About the port:

  • If the model service listens on the default HTTP port 80, you can omit the port entirely. Example: my-model-service

  • If it uses a non-standard port (most often 8000 for vLLM, llama.cpp, Ollama with a custom port, etc.), you must specify the port. Example: my-model-service:8000

  • The most common case inside Kubernetes: when models are running in the same cluster, use the Kubernetes service name (without external IP).

You can also set:

  --set secrets.VLM_MODEL_NAME="mistralai/Mistral-Small-3.2-24B-Instruct-2506" \
  --set secrets.VLM_TOKEN="your_access_token" \

These parameters are optional. Use them when needed:

  • VLM_MODEL_NAME: Use when your deployment includes several LLMs.

  • VLM_TOKEN: Use when your VLM requires an access token.

Wait until all pods are Ready, then port-forward to the UI with the following command:

kubectl port-forward svc/$name-aimsb-fintech-onboarding-ui 8080:8080 -n $namespace

To clean up:

helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  --set vlm.existingService="129.212.190.161:8000" \
  | kubectl delete -f - -n $namespace

Connecting#

Option 1: Port Forwarding#

See the port-forward commands in the Quick Start sections above.

Option 2: HTTPRoute (Gateway Access)#

If your cluster has a Gateway API compatible gateway (for example, Kubernetes Gateway or Istio), you can enable HTTPRoute creation to route traffic through the gateway.

Prerequisites:

  • A Gateway named https must exist in the envoy-gateway-system namespace (or configure a different gateway).

  • The Gateway must be properly configured with listeners.

Enabling HTTPRoute:

Use --set http_route.enabled=true in the helm template command to enable HTTPRoute creation:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  --set http_route.enabled=true \
  # ... (other parameters as needed) ...
  | kubectl apply -f - -n $namespace

Obtaining the URL:

The URL to access the blueprint via HTTPRoute is formed by the chart name, release name, and the gateway’s hostname. Use this command to produce the URL by querying the hostname from the cluster:

echo "https://aimsb-fintech-onboarding-$name$(kubectl get gtw -A -o jsonpath='{.items[*].spec.listeners[?(@.name=="https")].hostname}' | tr -d \*)/"
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