Infrastructure Sizing
The recommendations on this page are approximate starting points based on AWS instance types. Actual requirements depend on workload characteristics, function count, and request concurrency. Use the self-managed-grpc-load-test guide to validate throughput and tune your control plane accordingly.
Infrastructure Components
The self-hosted NVCF control plane uses dedicated node pools, each isolated by a Kubernetes node selector label. Separating workloads onto dedicated pools prevents resource contention and simplifies scaling.
The table below lists the stateful infrastructure workloads deployed by the helmfile stack. These are the core dependencies that must be running before any NVCF services can start.
Each Cassandra replica must run on its own node to ensure high availability. Do not co-locate multiple Cassandra pods on the same node.
Node selectors are only applied when global.nodeSelectors.enabled is set
to true in the environment file. When disabled, all workloads are
scheduled without placement constraints.
Sizing Tiers
The tables below cover control-plane infrastructure only. GPU worker nodes are sized independently based on your inference workloads (see [GPU Worker Nodes]).
Development
For local development of the stack or functions, CI pipelines, or quick demos you can run the entire NVCF
stack on a single machine using k3d. This setup uses a single Cassandra replica,
fake GPUs, and ephemeral local-path storage.
See local-development for full step-by-step instructions.
Staging / Demo
A minimal deployment for validating the stack, running demos, or developing functions. All NVCF services can be co-located on one or two nodes with no dedicated node selectors.
Storage: ~50 GB EBS gp3
When to use:
- Initial evaluation and proof-of-concept deployments
- CI/CD pipelines and automated testing
- Demo environments
You can also run the full stack on your laptop using Kind or k3d. See local-development for instructions.
Minimal High Availability
The recommended minimum for production workloads. Core infrastructure (Cassandra, OpenBao) is fully redundant; the control plane has enough capacity for moderate function counts.
Total: 7 control-plane nodes
Storage: ~500 GB EBS gp3 (across Cassandra and OpenBao volumes)
When to use:
- Production workloads with up to ~2,000 registered functions
- Environments where core infrastructure HA is required
- Moderate request concurrency
Production (Full HA)
Designed for high-throughput production deployments with a large number of concurrent functions and full high availability across every component.
Total: 9—11 control-plane nodes
Storage: ~500 GB EBS gp3 (across Cassandra and OpenBao volumes)
When to use:
- High-throughput production with more than 2,000 concurrent functions
- Full HA required for all components including OpenBao
- Deployments serving multiple teams or tenants
The Production tier uses r5.2xlarge (memory-optimized) for Cassandra to
provide additional headroom for large datasets and compaction. For the control
plane, m5.4xlarge provides double the CPU and memory per node compared to
the Minimal HA tier, reducing the number of nodes needed while increasing
per-node headroom.
GPU Worker Nodes
GPU worker nodes are not part of the control-plane tiers above. They are sized based on your inference workloads and can be added to any tier.
Self-hosted NVCF supports any GPU instance type compatible with the NVIDIA GPU Operator.
GPU requirements:
- NVIDIA GPU Operator must be installed
- NVIDIA device plugin for Kubernetes
- Physical GPU hardware on worker nodes
For development and testing environments without GPUs, install the fake GPU operator to simulate GPU resources. See fake-gpu-operator for instructions.
Storage Recommendations
Storage sizes are configurable via the storageSize value in your environment
file. See helmfile-installation for details.
Some cloud providers have minimum PVC size requirements. For example, AWS EBS
gp3 volumes have a 1 Gi minimum.
Scaling Beyond Defaults
The default control-plane resource sizing shipped with the helmfile stack is designed to handle approximately 100 concurrent users. If you need higher throughput:
- Benchmark your deployment using the self-managed-grpc-load-test
guide. Start with
--vus 100and increase gradually. - Scale node pools independently. Cassandra, OpenBao, and control-plane pools can each be scaled without affecting the others.
- Increase pod resources for specific services by adding
values:blocks in the helmfile release definitions. See helmfile-installation for override examples.
- self-hosted-installation — Full installation walkthrough
- terraform-installation — Terraform-based cluster provisioning with
example
tfvarsfiles - self-managed-grpc-load-test — Validate control-plane throughput
