Qwen3 Model Stuck In Decode: A Bug Report & Discussion

Introduction

This document details a bug encountered with the Qwen3-235B-A22B-2507 model, where the prefill process occasionally halts while the model remains in a continuous decode state. This issue leads to a backlog of requests in the waiting queue, as the running queue becomes stuck with a single request. This article aims to provide a comprehensive overview of the problem, including the environment in which it was observed, steps taken to reproduce it (or the lack thereof), and relevant system information. The goal is to foster discussion and collaboration within the community to identify the root cause and potential solutions for this issue.

Bug Description

During prolonged operation of the Qwen3-235B-A22B-2507 model, an intermittent issue arises where the model ceases to perform the prefill stage and gets stuck in a continuous decode state. The running queue becomes occupied with a single request, preventing new requests from being processed and causing them to accumulate in the waiting queue. This behavior significantly impacts the model's throughput and overall performance. The issue has been observed in a specific environment, which will be detailed in the Environment section, but a reliable method to reproduce the bug consistently has not yet been determined. Further investigation and potentially debugging tools or logging mechanisms are needed to pinpoint the exact conditions that trigger this behavior. It is crucial to understand the underlying cause to develop effective mitigation strategies and prevent this issue from recurring in production deployments. Understanding the interaction between prefill and decode stages is essential for diagnosing this problem. The prefill stage processes the input prompt, while the decode stage generates the output tokens. An interruption in the prefill stage could stem from various factors, such as memory constraints, computational bottlenecks, or even software-level errors within the model's architecture or the underlying libraries it relies on. Debugging this issue requires a deep dive into the model's internal states and resource utilization patterns. Collecting logs and metrics related to memory usage, CPU and GPU utilization, and the timing of different operations within the model can provide valuable insights. Additionally, examining the input prompts that trigger this behavior might reveal patterns or specific characteristics that contribute to the issue. Collaboration with the broader community and the model developers is also vital to leverage collective expertise and identify potential solutions or workarounds.

Visual Evidence

Screenshots illustrating the issue are provided below:

These images visually demonstrate the state of the queues and the continuous decode operation, providing concrete evidence of the described bug. The first image highlights the stuck request in the running queue and the growing backlog in the waiting queue. The second image further emphasizes the continuous decode state, indicating that the model is not progressing through the prefill stage for new requests. These visual aids are crucial for understanding the severity and impact of the issue, as they clearly depict the operational bottleneck caused by the bug. Furthermore, these images serve as valuable references for developers and researchers who are working on diagnosing and resolving the problem. By examining the timestamps and other contextual information within the screenshots, it might be possible to correlate the issue with specific events or patterns in the system's behavior. This visual evidence, combined with other diagnostic data, can significantly accelerate the debugging process and lead to the identification of the root cause. Effective debugging often relies on a combination of visual analysis and data-driven insights.

Reproduction

Currently, there is no known method to reliably reproduce this issue. The occurrence appears to be intermittent and triggered by unknown factors during prolonged operation. Further investigation is required to identify the specific conditions that lead to this behavior. This lack of a consistent reproduction method poses a significant challenge for debugging and resolving the issue. Without a reliable way to trigger the bug, it becomes difficult to test potential fixes or experiment with different configurations. The intermittent nature of the problem suggests that it might be related to specific workload patterns, resource contention, or even subtle interactions between different components of the system. One approach to address this challenge is to implement comprehensive logging and monitoring mechanisms. By tracking various system metrics, such as CPU and GPU utilization, memory usage, network activity, and the timing of different operations within the model, it might be possible to identify correlations between these metrics and the occurrence of the bug. Another strategy is to create synthetic workloads that mimic the real-world usage patterns of the model. By subjecting the model to these synthetic workloads, it might be possible to stress the system in ways that trigger the bug more frequently. Additionally, collaboration with other users and developers who have encountered similar issues can be invaluable. Sharing experiences, insights, and potential workarounds can accelerate the process of identifying the root cause and developing effective solutions. Reproducibility is a cornerstone of effective debugging, and finding a way to consistently trigger this issue is a critical next step.

Environment

The issue was observed in the following environment:

• Python: 3.12.11
• CUDA: Available
• GPUs: 4x NVIDIA H20-3e
• GPU Compute Capability: 9.0
• CUDA_HOME: /usr/local/cuda
• NVCC: Cuda compilation tools, release 12.9, V12.9.86
• CUDA Driver Version: 550.144.03
• PyTorch: 2.8.0+cu129
• sglang: 0.5.3
• sgl_kernel: 0.3.14.post1
• flashinfer_python: 0.4.0rc3
• triton: 3.4.0
• transformers: 4.57.0
• torchao: 0.9.0
• numpy: 2.3.3
• aiohttp: 3.12.15
• fastapi: 0.118.0
• hf_transfer: 0.1.9
• huggingface_hub: 0.35.3
• interegular: 0.3.3
• modelscope: 1.30.0
• orjson: 3.11.3
• outlines: 0.1.11
• packaging: 25.0
• psutil: 7.1.0
• pydantic: 2.11.10
• python-multipart: 0.0.20
• pyzmq: 27.1.0
• uvicorn: 0.37.0
• uvloop: 0.21.0
• vllm: Module Not Found
• xgrammar: 0.1.24
• openai: 1.99.1
• tiktoken: 0.11.0
• anthropic: 0.69.0
• litellm: Module Not Found
• decord: 0.6.0
• NVIDIA Topology: GPU0 GPU1 GPU2 GPU3 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV18 NV18 NV18 PIX SYS 0-55,112-167 0 N/A GPU1 NV18 X NV18 NV18 NODE SYS 0-55,112-167 0 N/A GPU2 NV18 NV18 X NV18 NODE SYS 0-55,112-167 0 N/A GPU3 NV18 NV18 NV18 X NODE SYS 0-55,112-167 0 N/A NIC0 PIX NODE NODE NODE X SYS NIC1 SYS SYS SYS SYS SYS X

Legend:

X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks

NIC Legend:

NIC0: mlx5_0 NIC1: mlx5_1

• ulimit soft: 1048576

This detailed environment information is crucial for others attempting to reproduce the issue or identify potential conflicts or compatibility problems. The specific versions of Python, CUDA, PyTorch, and various other libraries can significantly impact the behavior of the model. The NVIDIA topology provides insights into the communication pathways between GPUs and network interfaces, which can be relevant if the issue is related to data transfer bottlenecks or inter-GPU synchronization. The ulimit soft setting indicates the maximum number of open file descriptors, which could be a factor if the model involves extensive file I/O operations. The absence of vllm in the list of installed modules might also be relevant, as it suggests that a specific optimization or inference backend is not being used. By providing this comprehensive environment snapshot, the chances of successfully reproducing the issue in a similar setting are greatly increased. This also allows developers to focus their attention on potential environment-specific factors that might be contributing to the problem.

Additional Information

The following checklist confirms that several preliminary steps have been taken:

• [x] I searched related issues but found no solution.
• [x] The bug persists in the latest version.
• [x] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.
• [x] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.
• [x] Please use English. Otherwise, it will be closed.

These confirmations indicate that the issue is not a known problem with existing solutions and that the necessary information has been provided for effective troubleshooting. The search for related issues ensures that the problem is not a duplicate or a previously addressed concern. The persistence of the bug in the latest version suggests that it is not a regression introduced by a recent update. The inclusion of environment information is crucial for context and reproducibility, while the explicit statement about providing a minimal reproducible demo (or the lack thereof) highlights the challenges in debugging this particular issue. By adhering to the guidelines and best practices outlined in the checklist, the bug report is more likely to receive prompt attention and effective feedback from the community and the developers. A well-prepared bug report significantly increases the chances of a successful resolution.

Conclusion

The Qwen3-235B-A22B-2507 model exhibits an intermittent issue where the prefill process halts, causing the model to get stuck in a continuous decode state. This behavior leads to a backlog of requests and significantly impacts performance. While the exact cause and reproduction steps remain elusive, detailed environment information and visual evidence have been provided to aid in the investigation. Further research, debugging, and community collaboration are necessary to identify the root cause and develop effective solutions. Your insights and experiences are highly valued in resolving this issue. If you have encountered similar problems or have any suggestions, please feel free to contribute to the discussion. Let's work together to enhance the stability and reliability of the Qwen3-235B-A22B-2507 model.

For more information on debugging and troubleshooting complex software issues, consider exploring resources such as the Debugging Rules website.