Skip to main content
Your DGX Spark already runs the model fine. The hard part is using it from somewhere else without exposing the box to the internet or hand-building a gateway. Run the Tokios connector next to your runtime and you get one authenticated endpoint at https://api.tokios.com that speaks both OpenAI and Anthropic request formats, so your laptop, Claude Code, and anyone you invite can reach the Spark without touching firewall rules.

​
The gap after “it runs”

DGX Spark and GB10 owners typically get a model running with Ollama, llama.cpp, or vLLM, then hit a second problem: using that model from anywhere other than the Spark itself.
  • Plumbing. Getting one OpenAI-compatible endpoint out of a multi-model setup often means wiring together LiteLLM, llama-swap, and the runtime itself, plus a polling daemon to keep them in sync — components some DGX Spark community projects describe as the explicit goal, and the setup as “plumbing” they’d rather skip.
  • Remote access. The common paths to reach the Spark from another machine — binding the runtime to 0.0.0.0 (snap set ollama host="0.0.0.0:11434"), an SSH tunnel, or Tailscale — each carry their own fiddly setup on ARM64 / DGX OS, and a raw bound port has no per-client auth in front of it.
  • API shape. Claude Code speaks the Anthropic Messages format; local runtimes speak OpenAI Chat Completions. Bridging the two usually means writing and maintaining a small proxy.
Tokios is the connect layer that sits on top of the runtime you already run — it does not replace Ollama, llama.cpp, or vLLM.

​
Can a DGX Spark cluster run Kimi K3?

No. Kimi K3’s 2.8T parameters (~50B active per token through MoE) need roughly 1.4 TB at their native 4-bit floor, while NVIDIA’s documented Spark clusters top out at 4 nodes and 512 GB — and Moonshot targets K3 at supernodes with 64 or more accelerators. Even 8 DGX Sparks (1 TB pooled) fall 376 GB short of just the raw weight floor. For Spark today, choose a model within NVIDIA’s documented 120B single-node, 400B two-node, or 700B four-node ranges — see DGX Spark model compatibility for the full list. For the complete K3 memory calculation and cluster math, see the Kimi K3 hardware deep dive.

​
Set it up on your Spark

1

Start your runtime on the Spark

Run your model server as you normally would, for example:
or
Confirm it answers on its own OpenAI-compatible /v1 API before adding the connector.
2

Get a claim code and pair the connector

In the dashboard, open the Setup tab and select Start pairing to get a one-time claim code shaped TKS-XXXX-XXXX (valid for about 15 minutes). On the Spark, run:
Tokios ships a Linux arm64 build of tokios-connector, which covers DGX Spark’s Grace CPU. The exact systemd integration and packaging steps can vary by DGX OS version — check yours before scripting the install.Back in the dashboard, approve the device on the Setup or Connectors tab. The connector’s token is delivered over the tunnel — you never paste it into a file.
3

Point the connector at your runtime's /v1 endpoint

The connector’s upstream is the runtime’s own OpenAI-compatible API on the Spark, not a port you expose externally. Default ports: Ollama 11434, vLLM 8000 (both with /v1 appended). No inbound firewall rule is needed — the connector only dials out.
4

Register a deployment

On the Models tab, map your upstream model id (for example llama3.1) to a public deployment name such as spark-llama. Clients send this deployment name in the model field, not the upstream id.
5

Create an API key

On the Keys tab, create a key. Keys are sk-tok-… Bearer tokens, shown once, and can be scoped to specific deployment name patterns.

​
Use it from anywhere

Once the connector is paired and a deployment is registered, point your client at Tokios instead of the Spark’s IP address.
Claude Code’s model picker only lists claude* ids, so your Tokios deployment won’t appear in the list — pass --model spark-llama explicitly.
If you run more than one model on the Spark, or split traffic across Ollama, llama.cpp, and vLLM at once, use the connector config file to define multiple routes and upstreams instead of the single-upstream pairing flow. See Model routing for how to register and switch between several deployments behind the same endpoint.

​
Why one endpoint

  • Swap models without touching client config. Point a deployment name at a different upstream model or backend, and every agent and script that already targets spark-llama keeps working.
  • Scoped keys for teammates. Give a teammate a key limited to specific deployment patterns instead of sharing raw access to the Spark.
  • No inbound ports on the Spark. The connector only makes outbound connections, so you don’t bind the runtime to 0.0.0.0 or maintain an SSH tunnel or Tailscale node just to reach it from a laptop.

​
Next steps

Quickstart

Go from zero to a working connector, deployment, and API key.

Install the connector

Full connector install and pairing reference for all platforms.

Use Claude Code

Configure ANTHROPIC_BASE_URL and ANTHROPIC_API_KEY in detail.

Connector config reference

Serve multiple models from one Spark with tokios-connector.json.