AGAnchorGPU

Deployments

Choose a machine image

Choose an image compatible with your GPU vendor and workload from the current catalog.

3 min read · Updated

Start with the workload

Choose a general-purpose image if you intend to install your own stack, or a framework image for a specific workflow. The table below is generated from the same catalog as the deployment selector.

The available image list changes with the GPU vendor. Switching to an incompatible vendor resets the selection to a compatible image. The server also validates compatibility, including requests made directly through the API.

Images in the catalog

These labels describe the configured image templates. The local demo does not boot containers or validate the installed packages of a remote machine.

ImageCatalog labelGPU vendors
Ubuntu 24.04ubuntuCUDA readyNVIDIA, AMD
PyTorch 2.8pytorchCUDA 12.8NVIDIA
vLLMvllmOpenAI-compatible APINVIDIA, AMD
ComfyUIcomfyuiFlux & SDXLNVIDIA
OllamaollamaLocal model serverNVIDIA, AMD
JAXjaxCUDA 12NVIDIA
ROCm 6.4rocmAMD optimizedAMD
Debian 13debianMinimalNVIDIA, AMD

Keep workloads reproducible

For a real workload, record the driver, runtime, framework version and model dependencies in your project. A template name alone is not a reproducible environment. Keep your training data and outputs backed up outside the rented node.

Configure an image