> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aireserve.com/llms.txt
> Use this file to discover all available pages before exploring further.

# GPU Infrastructure

> Provision raw GPU compute with predictable pricing, durable operations, and provider-neutral controls.

GPU Infrastructure gives you a container on dedicated GPU capacity for training,
batch jobs, development, and self-hosted services. Choose the accelerator and
spending boundary; AI Reserve handles provider placement, current stock, and
price checks.

> **[Launch your first GPU](/gpu-infrastructure/quickstart)**
>
> Quote, launch, connect over SSH, and clean up safely.

> **[Serve a frontier model](/gpu-infrastructure/playbooks)**
>
> Follow versioned GLM, Kimi, and DeepSeek deployment playbooks from weights to production acceptance.

> **[Configure an instance](/gpu-infrastructure/launch-instances)**
>
> Choose hardware, region, image, ports, and storage.

> **[Connect and manage](/gpu-infrastructure/manage-instances)**
>
> Inspect state, retrieve connections, stream logs, and control lifecycle.

> **[Keep files between runs](/gpu-infrastructure/storage)**
>
> Understand ephemeral root disks and persistent volumes.

> **[Launch a cluster](/gpu-infrastructure/clusters)**
>
> Run large models across interconnected GPU nodes with one quote and lifecycle.

## Pick the right GPU product

| You need | Use | What you manage |
| - | - | - |
| A GPU machine for training, development, batch work, or a custom service | **GPU Infrastructure** | Container image, files, ports, connections, and lifecycle |
| A model served behind an OpenAI-compatible inference endpoint | [**Dedicated GPU endpoints**](/capabilities/dedicated-gpu-endpoints) | Model and deployment lifecycle; AI Reserve manages the serving endpoint |
| 2–8 interconnected machines for distributed training or a model that does not fit one node | [**Multi-node clusters**](/gpu-infrastructure/clusters) | Same as an instance, applied to every node; one shared volume |

GPU Infrastructure is raw compute. It does not autoscale your application or
turn a container into a managed inference API.

## The core workflow

**Request → quote → launch.** Describe the accelerator, region, spending
boundary, and storage lifecycle you need. AI Reserve returns the complete hourly
price before creating anything, then exposes connection and lifecycle controls
through one normalized instance resource.

Follow the [quickstart](/gpu-infrastructure/quickstart) for a complete SSH
round trip, or read [Configure and launch](/gpu-infrastructure/launch-instances)
for every request control and quote caveat.

Deploying open weights? Start with the
[frontier model serving playbooks](/gpu-infrastructure/playbooks) for pinned
artifacts, model-specific runtimes, behavioral tests, and the production
acceptance bar.

## What stays stable

The Instances API uses provider-independent accelerator and region names.
Adding another infrastructure backend does not change the request shape or your
resource IDs. Instance responses intentionally expose normalized lifecycle,
storage, price, and connection information rather than provider placement IDs.

| Surface | Start here |
| - | - |
| CLI | `aireserve gpu instances` with `quote`, `launch`, `list`, `get`, `connections`, `start`, `stop`, or `terminate` |
| REST | `/v1/gpu/instances`, `/v1/gpu/instances/quote`, and `/v1/gpu/operations/{operationId}` |
| Storage | `aireserve gpu volumes` with `list`, `create`, `get`, `grow`, or `delete`; REST uses `/v1/gpu/volumes` |
| Clusters | `aireserve gpu clusters` with `quote`, `launch`, `list`, `get`, `connections`, `logs`, or `terminate`; REST uses `/v1/gpu/clusters` |
| Reference | Generated request and response schemas under [API Reference](/api-reference) |

> [!WARNING]
>
> Running instances accrue compute charges. Persistent volumes accrue storage charges until you delete them, including
> while detached or while an instance is stopped.


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