Qwen3.5-35B-A3B Users Bypass Thinking Module for Faster Instruct Mode Performance

In a significant development for local AI deployment, users of the Qwen3.5-35B-A3B large language model have discovered a method to bypass the model’s built-in "thinking" mechanism—resulting in faster response times and improved efficiency for instruction-following tasks. The technique, first documented on the r/LocalLLaMA subreddit by user guiopen, involves appending the flag --chat-template-kwargs '{"enable_thinking": false}' to the llama.cpp server startup command. This modification effectively disables the model’s internal chain-of-thought reasoning layer, which typically generates intermediate reflections before delivering final responses.

According to the original poster, the performance gain is substantial without a noticeable degradation in output quality. "Overall it is still very good in instruct mode, I didn’t notice a huge performance drop like what happens in GLM-Flash," the user noted. This observation is particularly noteworthy given that other recent models, such as Zhipu AI’s GLM series, have exhibited significant accuracy trade-offs when similar reasoning modules are disabled. The Qwen3.5-35B-A3B, a 35-billion-parameter model released by Alibaba’s Tongyi Lab and hosted on Hugging Face, appears to maintain robust instruction-following capabilities even without its internal reasoning scaffold.

To maximize results, guiopen also recommends a specific parameter set optimized for instruct use cases: --repeat-penalty 1.0 --presence-penalty 1.5 --min-p 0.0 --top-k 20 --top-p 0.8 --temp 0.7. These settings reduce redundancy, encourage diversity in output, and temper randomness—aligning with Qwen’s official guidance for conversational and task-oriented applications. The absence of a high temperature (0.7 is moderate) and the use of top-k sampling (20) suggest a deliberate balance between creativity and precision, making this configuration ideal for enterprise chatbots, code generation, and document summarization tasks deployed on consumer-grade hardware.

While the "thinking" module was originally designed to improve reasoning accuracy in complex multi-step problems—such as mathematical proofs or logical deductions—the new workaround demonstrates that for many real-world applications, direct instruction response is not only sufficient but preferable. This is especially true in latency-sensitive environments like mobile apps, edge devices, or real-time customer service systems, where speed trumps exhaustive deliberation.

Hugging Face’s model card for Qwen3.5-35B-A3B confirms the model’s training on diverse instruction datasets and its suitability for both chat and non-chat scenarios, though it does not explicitly document the enable_thinking toggle. The fact that this parameter is accessible via llama.cpp suggests that the model’s architecture retains modular control over its reasoning pipeline, a design choice that may have been intentional to allow for flexible deployment scenarios.

Industry analysts suggest this discovery could influence future LLM development. "It reveals that the assumption that internal reasoning is always necessary for high-quality outputs may be overstated," said Dr. Elena Ruiz, a machine learning researcher at Stanford’s AI Lab. "If a model can perform competitively without its reasoning layer, it implies that much of the cognitive overhead we’ve built into LLMs might be redundant for common tasks. This could lead to leaner, faster models in the next generation."

As of now, the method is not officially endorsed by Alibaba, but its widespread adoption among local AI enthusiasts signals a grassroots optimization trend. Developers using Ollama, Text Generation WebUI, and other llama.cpp-based interfaces are already incorporating these settings into their templates. The Qwen3.5-35B-A3B’s resilience under this configuration positions it as a leading candidate for lightweight, high-performance AI deployment—especially in regions with limited cloud access or strict data sovereignty requirements.

For developers seeking to replicate this setup, the steps are straightforward: update the llama.cpp server command with the specified flags, ensure the model is properly quantized (e.g., Q4_K_M or better), and validate outputs against benchmark tasks. Early adopters report up to 30% faster token generation with no measurable loss in task completion rates for common prompts.

This development underscores a broader shift in the AI community: the move from "bigger is better" to "smarter deployment." As models grow in size, the ability to surgically disable non-essential components may become as important as the models themselves.