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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

Quote, launch, connect over SSH, and clean up safely.

Serve a frontier model

Follow versioned GLM, Kimi, and DeepSeek deployment playbooks from weights to production acceptance.

Configure an instance

Choose hardware, region, image, ports, and storage.

Connect and manage

Inspect state, retrieve connections, stream logs, and control lifecycle.

Keep files between runs

Understand ephemeral root disks and persistent volumes.

Launch a cluster

Run large models across interconnected GPU nodes with one quote and lifecycle.

Pick the right GPU product

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 for a complete SSH round trip, or read Configure and launch for every request control and quote caveat. Deploying open weights? Start with the frontier model serving 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.
Running instances accrue compute charges. Persistent volumes accrue storage charges until you delete them, including while detached or while an instance is stopped.