# Become a Provider This guide walks through turning your GPU into an AI inference endpoint on Swan Chain — from starting a local model server, to installing the `computing-provider` agent, to earning stablecoin revenue from real inference traffic. {% hint style="info" %} Looking to **consume** models instead of provide? See [How to Use Swan Inference](/core-concepts/swan-2.0-inference-cloud/how-to-use.md). For hardware tiers, collateral economics, revenue splits, and slashing rules, see the [Provider Onboarding](/core-concepts/swan-2.0-inference-cloud.md#provider-onboarding) section of the Swan 2.0 overview. This page focuses on the hands-on setup. {% endhint %} ## 0. Check prerequisites Providers connect **outbound** to Swan Inference over WebSocket — **no public IP, domain, or SSL setup is required**. You just need a capable GPU and one of two supported OS/inference-engine stacks: | Platform | Minimum hardware | Inference engine | | ------------------------- | ---------------------------------------------------------- | ------------------------------------------------------------------------------ | | **Linux (NVIDIA)** | GPU with ≥ 8 GB VRAM (Tier C); 24 GB+ recommended (Tier A) | [SGLang](https://github.com/sgl-project/sglang) (recommended), vLLM, or Ollama | | **macOS (Apple Silicon)** | M1/M2/M3/M4 with ≥ 16 GB unified memory | [Ollama](https://ollama.com) | Legacy GPUs (TESLA P4, GTX 1050 Ti, anything < 8 GB VRAM) cannot serve modern inference workloads and will not receive traffic. Full tier-to-model mapping is in [Hardware Tiers](/core-concepts/swan-2.0-inference-cloud.md#hardware-tiers). You'll also need: * **Go 1.22+** to build the `computing-provider` agent * **Docker 24.0+ with the NVIDIA Container Toolkit** (Linux only) * A funded wallet or credit card for collateral (step 5) ## 1. Start a model server Your GPU needs an OpenAI-compatible inference server running locally. Swan Inference will route requests to it via the `computing-provider` agent. ### Linux (NVIDIA) — SGLang ```bash # Download model weights from HuggingFace computing-provider models download Qwen/Qwen2.5-7B-Instruct # Start SGLang serving the model on port 30000 docker run -d --gpus all -p 30000:30000 --ipc=host --name sglang \ -v ~/.swan/models/Qwen/Qwen2.5-7B-Instruct:/models \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server --model-path /models \ --host 0.0.0.0 --port 30000 \ --served-model-name Qwen/Qwen2.5-7B-Instruct ``` Verify it's healthy: `curl http://localhost:30000/v1/models`. ### macOS (Apple Silicon) — Ollama ```bash brew install ollama ollama serve & ollama pull qwen2.5:7b ``` Verify it's healthy: `curl http://localhost:11434/api/tags`. {% hint style="info" %} The quickstart uses Qwen 2.5 7B as an example, but earnings scale with real token traffic. Browse the [model catalog](https://inference.swanchain.io/models) to find in-demand models with less provider competition. {% endhint %} ## 2. Install the computing-provider agent Clone and build from source (mainnet): ```bash git clone https://github.com/swanchain/computing-provider.git cd computing-provider make clean && make mainnet && sudo make install # Verify computing-provider --version ``` Full install details including the NVIDIA Container Toolkit setup are in the [`computing-provider` README](https://github.com/swanchain/computing-provider#readme). ## 3. Run the setup wizard The wizard creates your provider account (or logs you into an existing one), auto-discovers your running model server, and writes `config.toml` and `models.json`: ```bash computing-provider setup ``` A typical run looks like this (macOS + Ollama): ``` ============================================================ Computing Provider Setup Wizard ============================================================ Step 1/5: Checking Prerequisites ------------------------------------------------------------ [ok] Ollama: ollama version is 0.14.1 (running) [!] Docker: Docker not running. Please start Docker daemon. (optional - Ollama available) [ok] GPU: Apple Silicon (Apple M1) Step 2/5: Initializing Configuration ------------------------------------------------------------ Node Name [demo-provider]: demo-provider Initialized CP repo at '/Users/swanchain/.swan/computing'. [ok] Configuration initialized Step 3/5: Authentication ------------------------------------------------------------ A Swan Inference account is needed to connect your provider to the network. Do you already have a Swan Inference account [y/N]: n Create a new Swan Inference account Email: demo-provider@example.com Password: Creating account... [ok] Account created! Set up your provider profile Provider Name [demo-provider]: Wallet Address (optional, press Enter to skip): -> Skipped - you can add a wallet later to start earning rewards Registering your provider... [ok] Provider registered! -> Provider ID: 84bca13d-d056-4826-a1ca-ac4d43597a9c -> Status: pending (your provider will be reviewed before it can earn rewards) [!] SAVE THIS API KEY - it connects your machine to Swan Inference and is only shown once. API Key: sk-prov-535a****fb47 Step 4/5: Discovering Model Servers ------------------------------------------------------------ [ok] Found ollama at localhost:11434 * qwen3:8b Matching with Swan Inference models... [ok] qwen3:8b -> Qwen/Qwen3-8B (100%) Step 5/5: Finalizing Setup ------------------------------------------------------------ Select models to enable: 1) Qwen/Qwen3-8B - ollama @ http://localhost:11434 ~16GB (local: qwen3:8b) Enter selections (e.g., 1,3,4) or press Enter for all [all]: [ok] Updated config.toml [ok] Created models.json ============================================================ Setup Complete! ============================================================ What to do next: * Start your provider: computing-provider run * Monitor in browser: computing-provider dashboard * Check connection: computing-provider inference status ``` **Save the `sk-prov-*` key** — it's shown once and authenticates this provider to the network. If you already have a `sk-prov-*` key (for example, from the web signup at [inference.swanchain.io/provider-signup](https://inference.swanchain.io/provider-signup)), pass it directly: ```bash computing-provider setup --api-key=sk-prov-xxxxxxxxxxxx ``` Config files land in `~/.swan/computing/`: * `config.toml` — WebSocket URL, API key, node name * `models.json` — mapping from Swan Inference model IDs to your local endpoints {% hint style="info" %} Consumer keys (`sk-swan-*`) and provider keys (`sk-prov-*`) are different. The `computing-provider` agent only accepts `sk-prov-*` keys. {% endhint %} ### Configuration reference The wizard writes sensible defaults, but if it failed to discover your model server, you run a non-standard port, or you want to serve multiple models, edit these files directly. **Provider config (`~/.swan/computing/config.toml`)** ```toml # ~/.swan/computing/config.toml [API] Port = 8085 MultiAddress = "/ip4//tcp/" NodeName = "" [RPC] SWAN_CHAIN_RPC = "https://rpc-proxima.swanchain.io" [Inference] Enable = true WebSocketURL = "wss://api-ws-dev.swanchain.io" ApiKey = "sk-prov-YOUR_API_KEY" Models = ["Qwen/Qwen2.5-7B-Instruct"] ``` **Model endpoints (`~/.swan/computing/models.json`)** ```json { "Qwen/Qwen2.5-7B-Instruct": { "endpoint": "http://localhost:30000", "gpu_memory": 16000, "category": "text-generation" }, "meta-llama/Llama-3.2-3B-Instruct": { "endpoint": "http://localhost:11434", "gpu_memory": 14000, "category": "text-generation", "local_model": "llama3.2:3b" } } ``` | Field | Required | Description | | ------------- | -------- | ------------------------------------------------------------------------ | | `endpoint` | Yes | URL of your local inference server (SGLang, vLLM, Ollama) | | `gpu_memory` | Yes | GPU VRAM used by this model in MB | | `category` | Yes | Model type: `text-generation`, `image`, `embedding`, `audio` | | `local_model` | No | Local model name if different from the key (e.g., Ollama's `qwen2.5:7b`) | | `api_key` | No | API key if your model server requires authentication | The keys in `models.json` must match valid Swan Inference model IDs. Run `computing-provider models catalog` or check the [model catalog](https://inference.swanchain.io/models) for the full list. The agent watches `models.json` and hot-reloads on change — no restart needed. You can also force a reload: ```bash curl -X POST http://localhost:8085/api/v1/computing/inference/models/reload ``` ## 4. Start the provider and pass benchmarks Run the agent: ```bash nohup computing-provider run >> cp.log 2>&1 & ``` Then check your status: ```bash computing-provider inference status ``` You'll move through these stages automatically: ``` Connect ──▶ Collateral ──▶ Approval ──▶ Active (instant) (see step 5) (< 24 hrs) (earning) ``` | Stage | What happens | Typical duration | | -------------- | ---------------------------------------------------------------------------------------------------------------- | ---------------- | | **Connect** | Agent opens a WebSocket to Swan Inference, registers your models, and auto-runs math / code / latency benchmarks | Instant | | **Collateral** | Deposit via Stripe or on-chain SWAN (step 5) | Instant | | **Approval** | Admin reviews your benchmark results and collateral | < 24 hours | | **Active** | Traffic starts flowing — you earn per-request revenue | Ongoing |
Provider activation stages shown in the dashboard

The My Provider tab visualizes the activation flow: Start → Connect → Deposit Collateral → Approved → Active & Earning.

## 5. Deposit collateral Once approved, deposit collateral to unlock full traffic routing. Two options: | Method | Currency | Processing | Refund | | ------------ | --------------------------- | -------------------- | ------------------------------------------- | | **Stripe** | Credit/debit card (USD) | Instant | 7-day waiting period, back to original card | | **On-chain** | SWAN tokens on Swan Mainnet | Requires SwanETH gas | 7-day waiting period, back to your wallet | ```bash # Show instructions for your account (deposit address, minimum amount) computing-provider inference deposit # Verify deposit was seen on-chain computing-provider inference deposit --check ```
Provider collateral deposit panel

Provider dashboard's Collateral Deposit panel — verify your wallet, then pay the required amount via Stripe or on-chain crypto.

Collateral amounts scale with hardware tier and earning multiplier. See [Computing Provider Collateral](/core-concepts/token/computing-provider-collateral.md) for the full table. ## 6. Monitor earnings and uptime The Provider dashboard at [inference.swanchain.io/dashboard](https://inference.swanchain.io/dashboard) shows live earnings, request counts, and benchmark history.
Provider earnings dashboard

Earnings dashboard with live request volume, per-model breakdown, and payout history.

For a local view, the agent ships its own web dashboard: ```bash computing-provider dashboard # → http://localhost:3005 ``` Set where payouts go: ```bash computing-provider inference set-beneficiary 0xYourWalletAddress ``` {% hint style="info" %} **New Provider Grace Period:** For the first 7 days after activation, uptime and success-rate deprioritization are waived. Use this window to stabilize your setup before full routing weight kicks in. {% endhint %} ## Switching or adding models Edit `~/.swan/computing/models.json` — the agent watches this file and hot-reloads without restarting. Start additional model servers on different ports and add them all to the JSON. Full walkthrough with multi-GPU pinning is in the [`computing-provider` README](https://github.com/swanchain/computing-provider#switching-models). ## Troubleshooting | Symptom | Fix | | ----------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `invalid provider API key` | Verify key starts with `sk-prov-` and check `ApiKey` in `~/.swan/computing/config.toml` | | `WebSocket connection failed` | Confirm outbound port 443 is open; URL must be `wss://` not `http://` | | Provider online but no requests | Model name mismatch — `--served-model-name` must exactly match the key in `models.json` and a model ID in the [catalog](https://inference.swanchain.io/models) | | `could not select device driver "nvidia"` | Install the NVIDIA Container Toolkit; see [`computing-provider` README](https://github.com/swanchain/computing-provider#install-nvidia-container-toolkit) | | Stuck in `pending` | Provider needs collateral + passing benchmark + hardware check. Run `computing-provider inference status` to see which condition is missing | Full troubleshooting catalog: [`computing-provider` README — FAQ](https://github.com/swanchain/computing-provider#faq). ## Next steps * [**Provider Onboarding**](/core-concepts/swan-2.0-inference-cloud.md#provider-onboarding) — hardware tiers, revenue split, slashing rules * [**Computing Provider Income**](/core-concepts/token/swan-provider-income.md) — contribution score formula and reward distribution * [**Computing Provider Collateral**](/core-concepts/token/computing-provider-collateral.md) — required amounts and refund process * [**Inference Marketplace**](/core-concepts/market-provider/inference-marketplace.md) — how pricing, routing, and settlement work under the hood Questions? Reach the team on [Discord](https://discord.gg/swanchain) or open an issue on the [`computing-provider` repo](https://github.com/swanchain/computing-provider/issues). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.swanchain.io/core-concepts/swan-2.0-inference-cloud/become-a-provider.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.