## Supported environments

| Deployment                         | Description                                                                                                                    |
| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
| [Amazon EKS 1.29+](https://docs.poolside.ai/deployment/cloud/aws-eks/overview)    | Amazon Elastic Kubernetes Service, with IAM Roles for Service Accounts (IRSA) and an Application Load Balancer               |
| [OpenShift 4.16+](https://docs.poolside.ai/deployment/cloud/openshift/overview)  | Red Hat OpenShift environments                                                                                                 |
| [Upstream Kubernetes 1.29+](https://docs.poolside.ai/deployment/cloud/upstream-kubernetes/overview) | Self-managed Kubernetes environments such as RKE2 or Charmed Kubernetes                                                         |
| [On-premises](https://docs.poolside.ai/deployment/on-prem/overview)             | Single-node Kubernetes (RKE2) on customer-provided or Poolside-provided hardware                                             |

## Inference requirements

Poolside [models](https://docs.poolside.ai/get-started/supported-models) have different minimum requirements. Use this table to size your inference nodes. For concurrent-agent capacity and developer-seat estimates, contact your Poolside account team.

| Model          | Quantization | Minimum GPU memory | Minimum CPU | Minimum host memory |
| -------------- | ------------ | ------------------ | ----------- | ------------------- |
| Laguna M.1    | FP8         | 384 GB             | 128 cores   | 1 TB                |
| Laguna XS.2   | FP8         | 96 GB              | 44 cores    | 512 GB              |

For local development on your own hardware, see [How to run a Poolside model locally](https://docs.poolside.ai/resources/run-model-locally). For cloud deployments, storage requirements depend on whether S3-compatible storage is colocated on the inference node. For on-premises deployments, see [Storage requirements](https://docs.poolside.ai/deployment/on-prem/storage) for guidance on sizing and configuring storage.

### GPU memory reference

Poolside model inference targets the following NVIDIA GPU families: RTX 6000 Blackwell, H100, and H200. The memory per GPU is listed here for reference against the per-model minimum GPU memory table above:

| GPU                             | Memory per GPU |
| ------------------------------- | ---------------- |
| H100                            | 80 GB           |
| RTX 6000 Blackwell             | 96 GB           |
| H200                            | 141 GB          |

Which GPUs suit a given model is model-dependent. The per-model minimum GPU memory table is a floor, not a GPU selector: context length, batch size, and the number of GPUs all affect what actually serves a model. Confirm the right combination of model, GPU type, and GPU count for your workload with your Poolside account team.

## Support and compatibility

For questions about integration with specific enterprise tooling or deployment workflows, contact Poolside.