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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11To run Qwen3.8-27B locally without generating thinking output, serve the official Qwen/Qwen3.8-27B checkpoint with a supported inference framework and set enable_thinking to false in the chat-template options for each request. For the model card’s local OpenAI-compatible example, the setting belongs under extra_body.chat_template_kwargs—not as a top-level field.
Choose a local inference framework
Qwen documents local deployment with Transformers, vLLM, SGLang, and TokenSpeed. Use the framework that fits your existing environment and hardware; the official examples provide different launch interfaces, not a performance ranking. Check the current documentation for your chosen framework because command syntax and supported options can change.
The official checkpoint is Qwen/Qwen3.8-27B. Qwen says its official weights are available from Hugging Face Hub and ModelScope, and its repository records the model availability announcement on 2026-08-14: Qwen3.8 repository.
Transformers: start an OpenAI-compatible local server
Qwen’s repository gives this server command:
transformers serve Qwen/Qwen3.8-27B --port 8000 --continuous-batchingThe documented OpenAI-compatible endpoint is http://localhost:8000/v1. Use that endpoint as the base URL in your local client. The model card also shows Transformers loading examples with pipeline("image-text-to-text", model="Qwen/Qwen3.8-27B") or with AutoProcessor and AutoModelForMultimodalLM; these are alternatives for applications that load the model directly rather than call a served endpoint.
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vLLM, SGLang, or TokenSpeed
The model card documents starting vLLM with:
vllm serve "Qwen/Qwen3.8-27B"It then demonstrates sending a chat-completions request to the local endpoint. Qwen also lists SGLang and TokenSpeed as local deployment options. Follow the launch and endpoint instructions for the framework version you install; the exact serving setup depends on your framework and environment.
Disable thinking in local chat requests
Thinking is enabled by default. In the model card’s local OpenAI-compatible chat-completions example, pass the setting inside extra_body:
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extra_body={"chat_template_kwargs": {"enable_thinking": False}}For example, the relevant part of a Python request using an OpenAI-compatible client is:
response = client.chat.completions.create(
model="Qwen/Qwen3.8-27B",
messages=[{"role": "user", "content": "Explain what a hash table is in two sentences."}],
extra_body={"chat_template_kwargs": {"enable_thinking": False}}
)Here, client must be configured to use your local server’s base URL and any authentication behavior required by that server. The model name and request options shown are based on Qwen’s model-card example; adapt client initialization to your framework.
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Do not confuse the local template syntax with Qwen Cloud’s API syntax. The model card specifies a direct enable_thinking: false field for Qwen Cloud requests, while the local OpenAI-compatible example nests the option under extra_body.chat_template_kwargs. Use the form documented for the endpoint you are actually calling.
Optional sampling settings for non-thinking mode
Qwen’s model card suggests these sampling values for non-thinking output. They are recommendations, not universal requirements:
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| Parameter | Suggested value |
|---|---|
temperature |
0.7 |
top_p |
0.80 |
top_k |
20 |
min_p |
0.0 |
presence_penalty |
1.5 |
repetition_penalty |
1.0 |
Qwen cautions that inference frameworks vary in which sampling parameters they support. If a setting is rejected or ignored, check the documentation for your installed framework and omit or translate unsupported parameters; enable_thinking is the separate setting that selects non-thinking mode.
What to know about memory and model capabilities
Qwen lists this as a 27-billion-parameter causal language model with a vision encoder and image and video understanding capabilities. Its model card specifies a native context length of 262,144 tokens and says the context can be extended to 1,000,000 tokens with RoPE scaling such as YaRN.
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Those specifications do not establish a universal minimum GPU memory or system RAM requirement. Memory needs depend on the checkpoint precision or quantization, context length, and serving configuration. Confirm compatibility against the exact checkpoint and framework you plan to use rather than treating the parameter count as a VRAM estimate.
Thinking generation and thinking-history retention are different
Use enable_thinking: false in the chat-template options to disable thinking generation for the request. The separate preserve_thinking setting concerns whether thinking is retained in conversation history; changing history retention is not a substitute for disabling thinking mode.
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