GPU rental reference

A rental is two objects:

A workload describes what to run: the image, the hardware, SSH keys, ports and disk. You can reuse it.
A deployment is one running instance of a workload. It is billed per minute while it is ready. Pulling the image and starting up are free, and so is a deployment that fails.

All endpoints live under https://api.green-compute.com and take Authorization: Bearer <key>, except where marked public.

Hardware

GPU id	VRAM	Price per GPU-hour
`rtx4090`	24 GB	$0.40
`rtx5090`	32 GB	$0.70

These are the current figures. The authoritative live source is GET /platform/pricing (public), which is what billing actually uses. To see how many GPUs are free right now, call GET https://control.green-compute.com/platform/v1/gpu-pool (public).

GPU ids are matched without regard to case or separators, so rtx4090, RTX 4090 and rtx-4090 are the same. GET /platform/nodes/supported (public) lists the ids you can rent.

RTX 5090 needs a CUDA 12.8+ image. The 5090 is Blackwell (sm_120). Images built for older CUDA, such as pytorch/pytorch:2.2.0-cuda12.1-*, start fine but fail the moment you use the GPU. pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime works on both cards.

Create a workload — `POST /platform/workloads`

{
  "name": "my-gpu-box",
  "kind": "pod",
  "image": "pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime",
  "requirements": {
    "gpu_count": 1,
    "supported_gpu_models": ["rtx4090"],
    "min_vram_gb_per_gpu": 24,
    "cpu_cores": 8,
    "memory_gb": 32
  },
  "metadata": {
    "ssh_public_keys": ["ssh-ed25519 AAAA... you@laptop"],
    "requested_ports": [8888],
    "volume_size_gb": 50
  }
}

Field	Meaning
`kind`	Must be `"pod"` for a rental.
`image`	Any public Docker image. SSH is injected at start-up, so the image does not need its own SSH server.
`requirements.gpu_count`	1 to 8 GPUs on a single machine.
`requirements.supported_gpu_models`	GPU ids you accept. Leave it empty to accept any; the quoted rate is then the highest.
`requirements.cpu_cores`, `memory_gb`	Capped at the machine's fair share for the number of GPUs you take.
`metadata.ssh_public_keys`	Your public keys. They are authorised for `root`. Recommended: no private key ever has to leave your machine.
`metadata.requested_ports`	Up to 10 container ports to expose (for example Jupyter on 8888). Their public host ports appear in the deployment's `port_mappings`.
`metadata.volume_size_gb`	Disk for `/workspace`, 10 to 2000 GB. Default 50.

The response contains workload_id.

Start it — `POST /platform/deployments`

{ "workload_id": "…", "save_on_exhaustion": true }

The response is the deployment. Fields worth reading:

Field	Meaning
`deployment_id`	Use this for every later call.
`state`	See the lifecycle below.
`hourly_rate_cents`	Price per GPU-hour, in cents. At creation it is the most this deployment can cost; once a GPU is assigned, it becomes that card's exact rate, which is never higher.
`deployment_fee_usd`	Always `0`. There is no deployment fee.
`port_mappings`	`{container_port: public_host_port}` once running.
`last_error`	Set if placement or start-up failed.

Starting requires at least one hour of the quoted rate times gpu_count in your balance. Otherwise you get 402 (see Errors).

save_on_exhaustion (default true): if your balance runs out, the pod is suspended with its disk kept, rather than deleted, so you can resume it after topping up.

Lifecycle

pending → scheduled → pulling → starting → ready
                                             │
                     suspended ◄── (balance ran out, save_on_exhaustion)
                                             │
                                  terminated ◄── DELETE

failed means it could not be placed or started. Read last_error. Poll GET /platform/deployments/{id} every 5–10 seconds; most rentals are ready within one to three minutes.

Connect — `GET /platform/deployments/{id}/ssh`

{
  "ssh_host": "…",
  "ssh_port": 40123,
  "ssh_username": "root",
  "ssh_command": "ssh root@… -p 40123",
  "private_key": "-----BEGIN OPENSSH PRIVATE KEY-----…"
}

If you supplied ssh_public_keys, connect with your own key and ignore private_key. The platform-generated key is returned only by this endpoint, and every retrieval is audit-logged. It never appears in create, get, list or delete responses.

Inside the pod:

the image's environment is preserved, so python, conda and CUDA_HOME behave as the image defines them, including for one-off commands like ssh … python train.py;
/workspace is your persistent disk;
nvidia-smi shows only the GPUs you rented.

Other calls

Call	Does
`GET /platform/deployments`	Lists your deployments.
`GET /platform/deployments/{id}`	One deployment.
`DELETE /platform/deployments/{id}`	Stops it and stops billing.
`POST /platform/deployments/{id}/resume`	Restarts a `suspended` pod in place, with its disk intact. Needs one hour of balance.
`DELETE /platform/workloads/{id}`	Deletes the workload definition.

Limits

30 workload creates and 30 deployment creates per minute per API key.
One GPU machine per deployment; gpu_count up to the machine size (8).

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