GPU instances

GPU instances are for training and serving models, and none can be provisioned yet because no GPU capacity is available in any region. GPU instances in the sidebar lists what Dekka plans to offer and takes waitlist requests. You can also request a private inference endpoint there, which the AI Gateway answers with a simulated reply outside production, so you can build against it before it runs.

Which GPU instances are planned?

Every instance is on the waitlist, and none has a price until a host quotes for the capacity.

Instance	id	GPUs	GPU memory	vCPU	Memory	Disk
L4, one GPU	gpu-l4-1	1 × L4	24 GB	8	32 GiB	200 GB
L40S, one GPU	gpu-l40s-1	1 × L40S	48 GB	16	64 GiB	400 GB
A100 80 GB, one GPU	gpu-a100-80gb-1	1 × A100 80 GB	80 GB	16	128 GiB	800 GB
A100 80 GB, four GPUs	gpu-a100-80gb-4	4 × A100 80 GB	320 GB	64	512 GiB	2000 GB

GET /v1/catalog/gpu-instances is public and returns each instance with its gpu_class, which is l4, l40s or a100-80gb, along with available set to false and availability set to waitlist. Creating a gpu_runtime resource answers 422 kind_not_available, and the error's details give the waitlist's path.

Join the waitlist

On an instance, choose Join the waitlist. Say how many you'd need under How many and what you'd run under What you'd run, add When you'd start if you know, and choose Join the waitlist. Nothing is reserved or charged, and Dekka contacts you when it can offer capacity.

The API takes the same request at POST /v1/orgs/{org_id}/gpu/requests, with a quantity from 1 to 64 and a use_case of up to 2000 characters. It also accepts a preferred region_id, and a kind of reservation to ask to reserve instances instead of joining the waitlist. An organisation can make 10 requests an hour.

curl https://api.dekka.cloud/v1/orgs/$ORG_ID/gpu/requests \
  -H "Authorization: Bearer $DEKKA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "offering_id": "gpu-l40s-1",
    "quantity": 2,
    "use_case": "Fine-tuning a speech model on Twi and Ewe recordings",
    "desired_start": "2027-01-15"
  }'

What do the request statuses mean?

Your requests are listed under Your requests, newest first.

In the dashboard	status	Meaning
Received	received	Dekka has the request
We've been in touch	contacted	Dekka has contacted you about capacity
Closed	closed	The request is settled

Withdraw a request

Choose Withdraw next to the request, or send DELETE /v1/gpu-requests/{id}. Dekka closes the request and stops listing it. A request that's already closed can't be withdrawn from the dashboard.

Request a private inference endpoint

A private inference endpoint serves an open-weights model for your organisation, and your code calls it through the AI Gateway like any other model. Under Private inference endpoints, choose Request an endpoint and fill in these fields, then choose Request endpoint.

• Name is lowercase letters, digits and dashes, up to 63 characters. It must be unique in the organisation and can't change later, because it's part of the endpoint's model ID.
• Model is the model's Hugging Face ID, such as meta-llama/Llama-3.1-8B-Instruct.
• GPU is a GPU class from the catalogue.
• Min replicas and Max replicas run from 0 to 8, and the maximum must be at least 1 and no lower than the minimum. A minimum of 0 lets the endpoint scale to zero when it's idle. New endpoints have 0 and 1.

The endpoint joins the waitlist straight away. The API creates one with POST /v1/orgs/{org_id}/inference-endpoints, which also takes a project_id to limit the endpoint to that project's keys, and an organisation can create 20 an hour. PATCH /v1/inference-endpoints/{id} changes the model, the GPU class or the replicas.

To delete an endpoint, choose Remove next to it or send DELETE /v1/inference-endpoints/{id}, and the gateway stops answering its model at once.

What do the endpoint states mean?

Every endpoint is on the waitlist for now. The other states apply once GPU capacity exists.

In the dashboard	state	Meaning
On the waitlist	waitlisted	The endpoint is waiting for GPU capacity
Provisioning	provisioning	The endpoint is starting on GPUs
Running	running	The endpoint is serving requests
Scaled to zero	scaled_to_zero	No replicas are running because the endpoint is idle
Failed	failed	The endpoint couldn't start or stopped working

Call an endpoint through the AI Gateway

Send private/<name> as the model, with a gateway key as the AI Gateway page describes. The dashboard shows this ID under the endpoint's name with a copy button, and the API returns it as gateway_model.

const completion = await client.chat.completions.create({
  model: "private/support-assistant",
  messages: [{ role: "user", content: "Summarise this complaint in one sentence." }],
});

Outside production, the gateway answers with a simulated reply that carries simulated: true, streamed or not, and no model runs. In production it answers 503 endpoint_unavailable until capacity exists, with availability and the endpoint's state in the error's details.

An endpoint limited to a project answers only that project's keys and keys for the whole organisation, and any other key gets 404 model_not_found. Routing rules and an AI Gateway node's allowed_models accept only catalogue models, so a project or node that names particular models refuses every endpoint with 403 model_not_allowed, and a project's allowed providers don't apply.

Requests to an endpoint appear under Requests with no cost, and they don't count against your AI tokens because nothing runs on a GPU. X-Dekka-Provider is private, and GET /v1/ai/models lists the endpoints your key may use with owned_by set to private.