The following points should be noted when using Qwen3-235B-A22B-Thinking-2507:
- hardware limitation: The BF16 version requires 88GB of video memory and the FP8 version requires 30GB of video memory. If resources are insufficient, reduce the context length or use multi-GPU parallelism (tensor-parallel-size parameter).
- inference mode: Context length ≥ 131072 is recommended for optimal performance to avoid duplicate outputs due to greedy decoding.
- Deployment method: Ollama or LMStudio is recommended for local runtime, but context length needs to be adjusted to prevent loop problems; vLLM/sglang is preferred for cloud deployment to improve throughput.
- Tool call security: When configuring external tools through Qwen-Agent, MCP file permissions should be strictly verified to avoid exposure of sensitive operations.
- version compatibility: Ensure that transformers ≥ 4.51.0, vLLM ≥ 0.8.5 and other dependent library versions, otherwise API errors may be triggered.
Long-term operation is recommended to monitor GPU memory and temperature, and enable quantization or slice-and-dice loading strategies if necessary.
This answer comes from the articleQwen3-235B-A22B-Thinking-2507: A large-scale language model to support complex reasoningThe
May not be reproduced without permission:AI productivity tools " What are the precautions when using Qwen3-235B-A22B-Thinking-2507?
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