Docs / Deploy a model

Deploy a model

Choose a model

The catalog lists curated open models (DeepSeek, Kimi, Qwen, Gemma and others) with a GPU shape that is known to run each one. You can also deploy an arbitrary Hugging Face repository: paste the repo id and pick a shape yourself.

Pick a shape and region

A shape is the GPU configuration the model runs on, for example one H100 or two H200s. The wizard shows shapes that fit the model, with an hourly and monthly price for each. Regions come from your linked provider. If your data must stay in the EU, pick an EU region: the deployment and its storage stay there.

Review and confirm

Before anything is provisioned you see a summary: the model, the shape, the region, and the estimated hourly and monthly cost. The prices shown are estimates from the catalog, not a quotation. Your cloud provider bills you directly for all infrastructure usage, which is why the confirm step asks you to acknowledge exactly that. Set a budget cap here too.

What happens during a boot

After you confirm, LLM Hangar provisions the instance in your account, configures the inference server (vLLM or SGLang), downloads the model weights, and brings the endpoint up. You can watch each step live in the dashboard. Large models take time on the first boot: weights for a big model are hundreds of gigabytes, so expect roughly 15 to 30 minutes cold for the larger catalog entries.

Where the provider supports it, LLM Hangar keeps a prepared stage with the weights already in place after a destroy. Redeploying the same model then reuses the stage and boots much faster. The stage's storage lives in your account and is shown with its own cost; you can delete it at any time.

When it is ready

A ready deployment shows its endpoint URL, for example https://abc12345.gw.llmhangar.com, with a valid certificate, plus an API key shown once. The Connect tab has copy-paste snippets for common clients: see using your endpoint.

Stop, resume, schedules

Deployments can be stopped and resumed without destroying them, and a wake/sleep schedule can keep one running only during working hours, which cuts the infrastructure bill accordingly. Deleting a deployment tears everything down in your account, and the teardown is verified: we check nothing is left running before reporting it destroyed.