AI Machines

AI Machines are one-click deploys of a GPU running an LLM. Pick a model, pick a mode, and the machine comes up serving inference. You choose one of two modes at deploy time.

AI Machines are the Standard tier. They are not confidential/TEE machines — for confidential (TEE) inference, use SGL Grid confidential inference.

Two modes

Private

Your own OpenAI-compatible endpoint. Just for you. The UI shows a branded HTTPS endpoint URL and an API key.

Join the grid & earn

Serve the network as a node and earn USDC + SGL. Requires 50,000 SGL staked.

Private mode

The machine runs llama-server (llama.cpp), which auto-exposes an OpenAI-compatible API on port 8080. You get:

Endpoint URL — https://ai.x402compute.cc/<instance-or-slug>/v1 for VM providers, or the provider TLS proxy for RunPod
API key — shown in the UI; sent as Authorization: Bearer <api_key>

Both /v1/chat/completions and /v1/models are served. That makes the endpoint OpenRouter-ready and compatible with any OpenAI-compatible client, app, agent, or router — point your existing SDK at the base URL and key.

from openai import OpenAI

client = OpenAI(
    base_url="https://ai.x402compute.cc/<instance-or-slug>/v1",  # from the UI
    api_key="<your-ai-machine-key>",     # from the UI
)

resp = client.chat.completions.create(
    model="<model-id>",
    messages=[{"role": "user", "content": "Hello from my AI Machine"}],
)
print(resp.choices[0].message.content)

# Discover the served model
curl https://ai.x402compute.cc/<instance-or-slug>/v1/models -H "Authorization: Bearer <your-ai-machine-key>"

# Chat
curl https://ai.x402compute.cc/<instance-or-slug>/v1/chat/completions \
  -H "Authorization: Bearer <your-ai-machine-key>" \
  -H "Content-Type: application/json" \
  -d '{"model": "<model-id>", "messages": [{"role": "user", "content": "hi"}]}'

Your endpoint works with any OpenAI-compatible client today. Listing it on OpenRouter as a public provider is a separate approval via OpenRouter’s own form — roadmap, not automatic.

Join the grid & earn

Deploy the machine as a network node instead of a private endpoint. It serves grid traffic and earns USDC + SGL. This mode requires 50,000 SGL staked. See Provide Compute for the full node-operator path.

GPU machines & regions

AI Machines run on GPU machines across multiple cloud providers and regions. You pick the plan and region at deploy time from the catalog; the network places the workload.

Fully managed

Auto-update — deployed machines keep themselves current (allowlist-gated, roughly every 6 hours).
Kept alive — the serving process is supervised and restarted (systemd) if it exits.
SSH access — you can still SSH in with the instance IP and a one-time root password from the UI/API. See Manage.

Billing

AI Machines bill by prepaid hours. The machine runs while it has prepaid time; extend it to keep it alive, or destroy it to stop billing. Payment is x402-native (also MPP and prepaid USD credits) — an agent with a funded wallet or prepaid compute credits can deploy and manage with no human in the loop.

Deploy via the control API

Base URL: https://compute.x402layer.cc AI Machines use the same control API as SGL Machines. On POST /compute/provision you add one nested object that selects the mode. The two modes are mutually exclusive:

Private endpoint — "ai_machine": { "model_id": "<model-id>", "mode": "private" }. mode must be "private".
Join the grid — "deploy_node": { "model_id": "<model-id>" }. This self-installs the box as a grid node owned by the paying wallet (Solana x402, stake-gated).

os_id is the provider GPU image id from the machine catalog (it varies by provider). prepaid_hours is an integer of at least 24.

Managed grid-node deploys require Solana x402 because the paying wallet becomes the staked operator wallet. Private AI Machines can use x402, MPP, or prepaid USD credits.

What an agent can select

Selection	How to choose it	Notes
Compute	Pick a `plan` from `GET /compute/plans`.	Plan rows include provider, GPU type/VRAM, vCPU, RAM, disk, hourly price, and valid regions.
Region	Set `region` from the selected plan’s `locations`.	RunPod uses `runpod`; VM providers use provider region ids.
Model	Set `ai_machine.model_id` for a private endpoint or `deploy_node.model_id` for a grid node.	The API rejects GPUs with too little VRAM before charging.
Port	Private AI Machines use the managed OpenAI-compatible server on port `8080`.	Agents receive the final HTTPS endpoint in the deploy response. Arbitrary port selection is not part of the AI Machine contract today.
Access	Pass `ssh_public_key`, `ssh_key_id`, or use the one-time password endpoint where supported.	DigitalOcean requires SSH keys. Vultr can expose a one-time password fallback. RunPod AI pods do not provide buyer SSH/root access.

curl -X POST https://compute.x402layer.cc/compute/provision \
  -H "Content-Type: application/json" \
  -H "X-Payment: <base64-signed-payment-payload>" \
  -d '{
    "plan": "<gpu-plan-id>",
    "region": "<region>",
    "os_id": <provider-gpu-image-id>,
    "prepaid_hours": 720,
    "network": "base",
    "ai_machine": { "model_id": "<model-id>", "mode": "private" }
  }'

The response includes an ai object with your endpoint details:

{
  "ai": {
    "model_id": "<model-id>",
    "api_key": "<your-ai-machine-key>",
    "port": 8080,
    "endpoint": "<endpoint>"
  }
}

The private endpoint is OpenAI-compatible at the returned endpoint value. VM providers use the branded HTTPS proxy (https://ai.x402compute.cc/<instance-or-slug>/v1); RunPod uses its provider TLS proxy. Authenticate with Authorization: Bearer <api_key>. The most important lifecycle call is extend — it keeps a running machine alive. Extend uses extend_hours (not prepaid_hours):

curl -X POST https://compute.x402layer.cc/compute/instances/<id>/extend \
  -H "Content-Type: application/json" \
  -H "X-Payment: <base64-signed-payment-payload>" \
  -d '{ "extend_hours": 24, "network": "base" }'

See the Control API reference for every endpoint and field.

An agent deploys + extends a machine via x402

The whole compute API is x402-native, so an agent with a funded wallet can run the full lifecycle with no human. When you call a paid endpoint without payment, the server answers 402 with a payment challenge; you settle and retry with an X-Payment header.

Call without payment → 402

curl -i -X POST https://compute.x402layer.cc/compute/provision \
  -H "Content-Type: application/json" \
  -d '{
    "plan": "<gpu-plan-id>",
    "region": "<region>",
    "os_id": <provider-gpu-image-id>,
    "prepaid_hours": 720,
    "network": "base",
    "ai_machine": { "model_id": "<model-id>", "mode": "private" }
  }'

HTTP/1.1 402 Payment Required
Content-Type: application/json

{ "x402": { "network": "base", "amount": "...", "payTo": "...", "asset": "..." } }

Sign the payment, retry with X-Payment → provisioned

The agent builds and signs the payment from the 402 challenge, then retries the same request with the X-Payment header:

curl -X POST https://compute.x402layer.cc/compute/provision \
  -H "Content-Type: application/json" \
  -H "X-Payment: <base64-signed-payment-payload>" \
  -d '{
    "plan": "<gpu-plan-id>",
    "region": "<region>",
    "os_id": <provider-gpu-image-id>,
    "prepaid_hours": 720,
    "network": "base",
    "ai_machine": { "model_id": "<model-id>", "mode": "private" }
  }'
# → 201 Created — provisioning started

Keep it alive — extend (same 402 → X-Payment flow)

Extend uses extend_hours (not prepaid_hours):

# 1) 402 challenge
curl -i -X POST https://compute.x402layer.cc/compute/instances/<id>/extend \
  -H "Content-Type: application/json" \
  -d '{ "extend_hours": 24, "network": "base" }'

# 2) settle + retry
curl -X POST https://compute.x402layer.cc/compute/instances/<id>/extend \
  -H "Content-Type: application/json" \
  -H "X-Payment: <base64-signed-payment-payload>" \
  -d '{ "extend_hours": 24, "network": "base" }'

MPP clients use Authorization: Payment instead of the x402 X-Payment header. Bare machines and private AI Machines can also pay from prepaid credits — top up once (POST /compute/credits/topup) and pass "use_credits": true. Managed grid-node deploys require Solana x402 because the payer is also the operator wallet.

Deploy

Launch a machine.

Manage

Extend, SSH, password, destroy.

Control API

Every compute endpoint.

Overview

User Guide

Slayer Agent

Core flows

Staking Engine

Singularity Cloud Network

Contract Addresses

ERC8004

Skills

Singularity Payments

Agentic Fundraisers

Enterprise

Two modes

Private

Join the grid & earn

Private mode

Join the grid & earn

GPU machines & regions

Fully managed

Billing

Deploy via the control API

What an agent can select

An agent deploys + extends a machine via x402

Deploy

Manage

Control API

​Two modes

Private

Join the grid & earn

​Private mode

​Join the grid & earn

​GPU machines & regions

​Fully managed

​Billing

​Deploy via the control API

​What an agent can select

​An agent deploys + extends a machine via x402

​Related

Deploy

Manage

Control API

Two modes

Private mode

Join the grid & earn

GPU machines & regions

Fully managed

Billing

Deploy via the control API

What an agent can select

An agent deploys + extends a machine via x402

Related