Computing Provider v2

Turn your GPU into an AI inference endpoint and join the Swan Chain decentralized computing network.

_{The built-in dashboard — computing-provider dashboard. Model names, payout rates, and earnings figures are example values.}

No wallet needed. No blockchain registration. No public IP required. The provider dials out to Swan Inference over a WebSocket, so it works behind NAT.

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Quick start

1. Install

# Linux x86-64 — also computing-provider-linux-arm64, computing-provider-darwin-arm64
curl -fL -o computing-provider \
  https://github.com/swanchain/computing-provider/releases/latest/download/computing-provider-linux-amd64
chmod +x computing-provider && sudo mv computing-provider /usr/local/bin/

computing-provider version

Building from source is optional — see installation.md.

2. Start a model server

Linux (NVIDIA GPU) — needs Docker with the NVIDIA Container Toolkit:

computing-provider models download Qwen/Qwen2.5-7B-Instruct

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

macOS (Apple Silicon):

brew install ollama
ollama serve &
ollama pull qwen2.5:7b

3. Set up and run

computing-provider setup             # auth, config, and model discovery
computing-provider run               # start serving
computing-provider inference status  # confirm you are connected

The wizard checks prerequisites, creates or logs into your Swan Inference account, discovers the model servers you just started, matches them to catalog model IDs, and writes config.toml and models.json. Start your model server before running it, or it will have nothing to find.

Earnings come from traffic, not hardware. Qwen 2.5 7B is only an example. Serving models with demand and few providers routes more requests to you — computing-provider inference recommend-models ranks the catalog against your GPU. See models.md.

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What happens after setup

Your provider moves through these stages on its own. Most are fully active within a day.

Connect ──▶ Benchmark ──▶ Approval ──▶ Collateral ──▶ Active
(instant)   (automatic)   (< 24 hrs)   (USD or crypto) (earning)

Stage	What happens	Time
Connect	Provider connects to the network and registers its models	Immediate
Benchmark	Automated benchmarks verify your GPU can serve the registered models	Minutes (automatic)
Approval	Admin reviews your provider	< 24 hours
Collateral	Deposit collateral to secure your position and unlock earnings (card, USDC on Ethereum or Base, or SWAN on Swan Chain)	Instant
Active	Start receiving inference requests and earning rewards	Ongoing

Grace period: new providers get 7 days after activation during which benchmark failures and low uptime do not affect routing priority, so you have time to stabilise your setup.

computing-provider inference status shows your current stage at any time.

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How it works

Swan Inference (Cloud)
        │
        │ WebSocket (outbound connection - works behind NAT)
        ▼
┌───────────────────────┐
│  Computing Provider   │
│  ┌─────────────────┐  │
│  │ Your GPU Server │  │
│  │ (SGLang/Ollama) │  │
│  └─────────────────┘  │
└───────────────────────┘

1. The provider connects outbound to Swan Inference — no inbound ports
2. It registers the models listed in models.json
3. Inference requests arrive over that WebSocket
4. Each is forwarded to your local model server and the response streamed back
5. You are paid per token for completed requests

Request flow is managed for you, with no tuning required to run: endpoints are health-checked continuously and unhealthy ones taken out of routing until they recover; GPU-aware rate limits and per-model concurrency slots reject excess work with HTTP 429 rather than overloading the GPU; transient upstream failures are retried with exponential backoff; models.json edits hot-reload; and SIGTERM/SIGINT drains cleanly. Limits can be retuned at runtime over the REST API.

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Configuration

Two files, both in $CP_PATH (default ~/.swan/computing), both written by computing-provider setup:

models.json maps catalog model IDs to your local servers:

{
  "Qwen/Qwen2.5-7B-Instruct": {
    "endpoint": "http://localhost:30000",
    "gpu_memory": 16000,
    "category": "text-generation"
  }
}

config.toml holds provider settings:

[API]
Port = 8085
NodeName = "my-provider"

[Inference]
Enable = true
WebSocketURL = "wss://inference-ws.swanchain.io"
ApiKey = "sk-prov-xxxxxxxxxxxxxxxxxxxx"   # from https://inference.swanchain.io
Models = ["Qwen/Qwen2.5-7B-Instruct"]

Every field, plus alerts, self-check, dashboard and logging settings, is documented in configuration.md.

Serving from Ollama, llama.cpp, LiteLLM or another proxy? Set context_length explicitly in models.json. Only vLLM and SGLang expose the real window, so otherwise the marketplace advertises the catalog's theoretical value and clients send prompts your backend rejects. See context windows.

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Monitoring

Self-check

The failures that cost a provider money are quiet: the daemon stays up and looks healthy while a model earns nothing. computing-provider selfcheck audits for exactly that.

  OK   models.json                     8 models mapped
  OK   config/models.json agreement    config.toml and models.json list the same models
  OK   Swan Inference connection       connected
  OK   registered with Swan Inference  all 8 models registered
  OK   model health                    all 8 models healthy
  OK   context window                  reported context matches every backend
 FAIL  inference probe                 openai/gpt-5.5: HTTP 401 authentication token has been invalidated
  OK   traffic                         every model has served requests
  OK   disk space                      /home/you/.swan/computing: 92.1 GB free of 438 GB (78% used)

It catches what no error message reveals: a model served but not advertised, a healthy model never registered upstream, a backend serving less context than you claim, one that answers health checks but cannot actually serve, a model that has never been called, and a disk about to fill.

The inference probe sends one max_tokens: 1 completion per model. It is the only check that exercises the engine — GET /v1/models is answered by most backends without touching it, so a dead engine or expired API token looks healthy everywhere else.

It exits non-zero on failure, so it works as a cron or monitoring check (--json for machine-readable output, --no-inference to skip the probe). The daemon runs the same audit every 10 minutes and acts on it: a model whose backend fails two consecutive probes is deregistered from Swan Inference and re-registered once it serves again — no traffic beats traffic that fails. Only backend-owned failures count; an over-long client prompt or your own rate limit never pulls a model, and one you disabled by hand is never switched back on. Tune it under [SelfCheck], see configuration.md.

Alerts

Point the provider at a webhook, an SMTP server, or both, and it tells you when something breaks:

[Alerts]
WebhookURL = "https://hooks.example.com/provider"

[Alerts.Email]
Host = "smtp.gmail.com"
Port = 587
Username = "you@example.com"
To = ["you@example.com"]

Keep the password out of config.toml — the provider reads $CP_PATH/.env at startup:

# Gmail, Outlook and Yahoo need an app password, not your login password
umask 077 && printf "SMTP_PASSWORD='your-app-password'\n" > $CP_PATH/.env
computing-provider alerts test    # verify before you need it

Alerts fire on a model going unhealthy, a model that passes health checks while failing most of its requests, a lost connection to Swan Inference, and a failed self-check — each with a matching recovery event. Passing runs are logged locally and never sent, so an "all clear" never trains you to ignore it. Payload and tuning in configuration.md.

Dashboard

computing-provider dashboard    # http://localhost:3060

Real-time metrics, earnings, model pricing, per-transaction token usage, GPU status, model management and request controls — the page shown at the top of this README.

It is read-only until you choose Unlock controls and paste the token from $CP_PATH/dashboard.token, an owner-only file generated on first run. Configuration, request limits, alerts, self-check and logging can then be edited from Settings; stored secrets are never sent back to the browser. Host and port are configurable under [Dashboard] — use 0.0.0.0 only on a network you trust.

Subscription-backed models

If you serve models through CLIProxyAPI — which turns a personal ChatGPT, Claude or Gemini subscription into an OpenAI-compatible endpoint — the provider can check and renew that backend's logins for you:

computing-provider cliproxy status           # account, plan and expiry per credential
computing-provider cliproxy status --probe   # and one real completion per model
computing-provider cliproxy login --device   # re-authenticate; prints a code and a URL

status reads credential metadata only — never a token — and exits non-zero on an expired or disabled login, so it works as a cron check.

Use --probe. A login can be unexpired, enabled, and still rejected by the subscription upstream, and the proxy answers /v1/models from a static list either way — so the model reports healthy while every request fails. Only a real completion tells those apart:

  expiring  codex   you@example.com (plus)   expires 2026-09-08 (in 128h0m0s)

Live probe
  HTTP 503  gpt-5.5
            auth_unavailable: no auth available (providers=codex, model=gpt-5.5)

That credential is not expired. Re-authenticating will not help — check whether the account still has access.

REST API

curl http://localhost:8085/api/v1/computing/inference/metrics

Endpoint	Description
GET /inference/status	Connection state, active models, what is registered upstream
GET /inference/metrics	Request counts, latency, GPU stats
GET /inference/metrics/prometheus	Prometheus format for Grafana
GET /inference/models	List all models with status
GET /inference/health	Health of every model
POST /inference/models/:id/enable	Enable a model
POST /inference/models/:id/disable	Disable a model
POST /inference/models/reload	Hot-reload models.json

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Getting paid

You are paid per token, for every request you serve. Each catalog model publishes a payout price per 1M input and output tokens; earnings accrue at that rate and appear in the Provider Dashboard and in computing-provider inference status. There is no UBI and no allocation for idle hardware — the Swan 1.0 UBI program has ended — so traffic is the only thing that earns.

computing-provider inference set-beneficiary 0xYourWalletAddress

Payouts are requested from the dashboard: minimum $10, flat $1 fee, one request per chain per hour. Earnings can also be converted into inference credit on the same account.

Collateral is required for activation, not optional. Deposit on-chain (USDC on Ethereum or Base, SWAN on Swan Chain) or by card; it is refundable with a 7-day waiting period.

computing-provider inference deposit          # chains, contracts, minimums
computing-provider inference deposit --check  # current collateral status

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CLI reference

computing-provider setup                     # interactive setup (recommended)
computing-provider run                       # start the provider
computing-provider selfcheck                 # audit this node
computing-provider dashboard                 # web UI on port 3060

computing-provider version                   # installed build
computing-provider update --check            # is a newer release available?
sudo computing-provider update               # install the newest release

computing-provider inference status          # stage, models, earnings, context
computing-provider inference config          # show inference config
computing-provider inference deposit         # collateral instructions
computing-provider inference recommend-models
computing-provider inference set-beneficiary 0x...

computing-provider models catalog            # supported models
computing-provider models download <id>      # fetch weights
computing-provider models list               # what is on disk

computing-provider cliproxy status --probe   # subscription-backed models: are they serving?
computing-provider cliproxy login --device   # re-authenticate one

computing-provider research hardware         # hardware, GPU info, benchmarks

setup also takes --skip-discovery and --api-key=sk-prov-xxx, and has discover, login and signup subcommands. Full reference in docs/cli/; --help works on every command.

Updates are verified against the checksums published with each release and the binary is replaced atomically. update deliberately does not restart a running provider — see keeping up to date.

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Documentation

Guide	Covers
Installation	Requirements, binary install, building from source, updates
Getting started	First run, Linux and macOS walkthroughs
Configuration	config.toml, models.json, alerts, self-check, dashboard
Models	Catalog, VRAM sizing, downloading weights, switching models
SGLang deployment	Running SGLang for inference
SGLang tuning	GPU configs, memory tuning, multi-GPU TP
Apple Silicon	Ollama setup on M-series Macs
CLIProxyAPI	Serving a ChatGPT/Claude subscription as an endpoint
Troubleshooting	Error reference and FAQ

Troubleshooting

Start with computing-provider selfcheck — it names the problem more precisely than the logs will. The two most common:

Symptom	Cause
Online but receiving no requests	--served-model-name, the models.json key, and the catalog ID must all match exactly
invalid provider API key	Provider keys start with sk-prov-; consumer keys (sk-swan-*) do not work

Full error reference and FAQ: docs/troubleshooting.md.

Getting help

• Discord — community support
• GitHub Issues — bug reports
• docs.swanchain.io — full documentation

License

Apache 2.0