> ## Documentation Index > Fetch the complete documentation index at: https://docs.seekr.com/llms.txt > Use this file to discover all available pages before exploring further. # Review SeekrFlow requirements > Everything a SeekrFlow Self-Managed deployment needs before you begin. SeekrFlow runs on a Kubernetes cluster in your own environment, on Amazon Web Services (AWS) or in your own data center. Before you begin, confirm you have everything on this page. Have your security team review [Security considerations](/self-managed/flow/security) before you provision anything. ## Kubernetes cluster SeekrFlow supports [conformant](https://github.com/cncf/k8s-conformance) Kubernetes clusters running one of the three most recent minor versions, listed on the Kubernetes [Releases](https://kubernetes.io/releases/) page. Versions a cloud provider maintains beyond that, such as Amazon Elastic Kubernetes Service (Amazon EKS) extended support, aren't supported. The cluster must run [Metrics Server](https://github.com/kubernetes-sigs/metrics-server). Under heavy load, SeekrFlow adds copies of its services, and Kubernetes uses Metrics Server to measure that load. Without it, SeekrFlow can't add copies. SeekrFlow works with policy engines such as Kyverno and Open Policy Agent (OPA) Gatekeeper. See [Set up networking and cluster policies](/self-managed/flow/provision/networking) for the settings your policies need to permit. ## Network connectivity SeekrFlow doesn't require a particular network design. It requires these connections: * The cluster to the container registry, object storage, PostgreSQL, OpenSearch, and any cloud model services you use. * Your users to the SeekrFlow load balancer. * The machine you install from to the Kubernetes API. If the API has no public endpoint, install from a machine inside your network, such as a bastion host. For every outbound connection SeekrFlow makes, see [Security considerations](/self-managed/flow/security#outbound-connections). ## Worker nodes Worker nodes run the platform services. For how many virtual CPUs (vCPUs) and how much memory they need, see [Size your deployment](/self-managed/flow/plan/size-deployment). | Requirement | Detail | | - | - | | Nodes | At least 3 if you enable the Valkey caches. Each cache runs three pods on separate nodes. | | Node type | Nodes you manage. Serverless compute, such as AWS Fargate, isn't supported. | | Root volume | At least 100 GiB. | | CPU architecture | amd64 (x86-64). arm64, including AWS Graviton, isn't supported. | ## GPU nodes GPU nodes run SeekrFlow's model servers, fine-tuning, and explainability workloads. Every deployment needs them. Amazon Bedrock can serve inference alongside them, but not fine-tuning or explainability. | Requirement | Detail | | - | - | | GPU vendor | NVIDIA. AMD and Intel GPUs are not supported. | | CPU architecture | amd64 (x86-64). arm64 isn't supported. | | GPU architecture | NVIDIA Ampere or newer, meaning CUDA compute capability 8.0 or higher, such as A10, L4, L40S, A100, or H100. To check another GPU, see NVIDIA's [CUDA GPU compute capability](https://developer.nvidia.com/cuda-gpus) list. | | NVIDIA driver | Version 580 or newer. SeekrFlow's container images include CUDA. You install only the driver on the nodes, not CUDA. | | GPU memory per node | Enough for the largest model you run on that node. A model can't span nodes. | | Root volume | At least 200 GiB, mainly for the model server images. Fine-tuning jobs require additional space. See [Size your deployment](/self-managed/flow/plan/size-deployment). | For how many GPUs your models need, see [Size your deployment](/self-managed/flow/plan/size-deployment). ## Services you provision | Service | What SeekrFlow uses it for | Set it up in | | - | - | - | | PostgreSQL 16 or later | Stores service data. | [Provision PostgreSQL](/self-managed/flow/provision/postgresql) | | OpenSearch | Stores the search indexes for file search and retrieval-augmented generation. AWS deployments provision it. On-premises deployments run it in the cluster. | [Provision OpenSearch](/self-managed/flow/provision/opensearch) | | Object storage | Stores datasets, model artifacts, and checkpoints. | [Provision storage](/self-managed/flow/provision/storage) | | Shared file storage | Caches model weights for GPU nodes. | [Provision storage](/self-managed/flow/provision/storage) | | Ingress | Routes your users to SeekrFlow, through an ingress controller, a DNS record, and a Transport Layer Security (TLS) certificate. | [Set up networking and cluster policies](/self-managed/flow/provision/networking) | | Secret store (optional) | Holds credentials outside the cluster. | [Set up secret management](/self-managed/flow/provision/secret-management) | ## Credentials and access | Item | From | Purpose | | - | - | - | | License key | Your Seekr account team | Validated at startup. Services don't run without it. | | Registry credentials | Your Seekr account team | Pulls the Helm chart and container images. | | Model configuration | Your Seekr account team | Defines which models to run on your GPU nodes and how to size them. | | Hugging Face access token | Your Hugging Face account | Downloads gated model weights. | | Brave Search API key | The Brave Search API | Lets agents run web searches. | | Access to Amazon Bedrock models (optional) | Your cloud account | Serves inference through externally hosted models. | | Kubernetes cluster-admin access | Your cluster administrator | Installs the chart's cluster-wide resources, such as custom resource definitions and roles. SeekrFlow has no reduced-permission install. | For how to get each one, see [Request credentials](/self-managed/flow/provision/checklist#request-credentials). ## Tools The install runs from a laptop or bastion host that can reach the cluster. | Tool | Used for | | - | - | | `sfe` command-line interface (CLI) | The recommended install path. Download it from Seekr's registry for Linux on amd64 or macOS on Apple silicon. See [Set up the sfe CLI](/self-managed/flow/prepare/sfe-cli). | | [`kubectl`](https://kubernetes.io/docs/tasks/tools/) | Inspecting the cluster, and the steps you run without `sfe`. | | [`helm`](https://helm.sh/docs/intro/install/) | Installing prerequisites such as the AWS Load Balancer Controller and External Secrets Operator. For the Helm install path, use Helm 3.2.0 or later. | | [AWS Command Line Interface (AWS CLI)](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) | Signing in to your AWS account, which `sfe` uses through the AWS SDK, and setting up resources without `sfe`. | | [Docker](https://docs.docker.com/get-started/get-docker/) | Signing in to registries before you mirror container images. Needed only if you mirror. | This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.