Open WebUI

The server is a plain OpenAI-compatible endpoint, so Open WebUI needs one connection and no plugins.

Connect

Admin Panel → Settings → Connections → OpenAI API → add:

• URL: http://<spark-host>:8000/v1 (if Open WebUI runs on the Spark itself and in Docker, use http://host.docker.internal:8000/v1, and change the port mapping in compose.yaml so the port is reachable from the Open WebUI container)
• Key: anything non-empty while disable_auth: true; the generated api_key otherwise

deepseek-v4-flash-0731 appears in the model picker once the connection is verified.

Make it feel fast

Two things dominate what a chat user perceives, and both are on the Open WebUI side.

Keep tools off the everyday model. Every tool or MCP server attached to a model row is serialised into every prompt. Forty tools is around ten thousand tokens of prompt per turn; at ~600 tok/s prefill that is fifteen seconds before the first character, on every message. Create two model entries from the same connection: one bare for chat, one with the tools for agent work. Also turn off Memory for the chat entry — its injected block sits at the start of the prompt and changes on every request, which defeats the prefix cache.

Leave the sampling alone. With temperature unset the server applies DeepSeek's recommended 1.0 / 1.0. Speed is a property of the output, not of the temperature: code streams at 50+ tok/s, prose at ~30, either way.

Thinking

Open WebUI renders reasoning_content as a collapsible thinking block. Thinking is off by default on the server. Open WebUI already sends its Reasoning Effort setting as a top-level reasoning_effort, so the simplest switch is that field: set it to high (or medium, max) in a model entry's Advanced Params, or per chat in Chat Controls, and that entry thinks. low (or unset) keeps thinking off. A separate "Thinking" entry with reasoning_effort: high next to the plain one is a good way to have both in the model picker.

The explicit form still works, via Custom Parameters (or a filter):

{"chat_template_kwargs": {"thinking": true, "reasoning_effort": "high"}}

Tokens of reasoning are billed against the same generation speed, so a high entry answers hard questions better and everything else slower.

Streaming looks "chunky"

Text arrives about six tokens at a time every ~150 ms. That is the draft model's step size, not buffering; the client-side and server-side tok/s figures in ./run.sh bench agree to within 2 %.