VLLM Bug: Deploying Fine-Tuned GPT-OSS Models

Aug 14, 2025 by Sebastian Müller 46 views

[Bug]: Fine-Tuned GPT-OSS Model Deployment Discussion

Introduction

This article delves into a specific bug encountered while deploying a fine-tuned GPT-OSS model using vLLM. We'll examine the environment setup, the steps taken to install vLLM, and the error encountered during the deployment process. This comprehensive analysis aims to provide insights into the issue and potential solutions for users facing similar challenges. The goal is to ensure smooth deployment and optimal performance of fine-tuned GPT-OSS models within the vLLM framework. Effective deployment of fine-tuned models is crucial for leveraging their capabilities in various applications, and understanding the intricacies of the deployment process is paramount. This article serves as a valuable resource for troubleshooting and resolving deployment issues, contributing to the broader community of AI practitioners and researchers. We'll cover everything from the initial setup to the final deployment attempt, offering a detailed account of the process and the encountered roadblocks.

Current Environment Setup

System Information

The system is running on Ubuntu 24.04.2 LTS (x86_64), utilizing GCC version 13.3.0 and a 64-bit runtime environment. The CPU is an Intel(R) Xeon(R) Platinum 8468, boasting 160 cores, which ensures robust computational power for demanding tasks. This configuration is well-suited for handling large-scale AI models and complex computations. The operating system and CPU specifications are critical factors in determining the overall performance and stability of the deployment environment. A properly configured system is essential for running vLLM and fine-tuned models efficiently. The choice of Ubuntu 24.04.2 LTS provides a stable and up-to-date platform, while the Intel Xeon Platinum 8468 CPU offers the necessary processing capabilities. This combination forms a solid foundation for deploying and running advanced AI models.

PyTorch Information

The PyTorch version installed is 2.9.0.dev20250804+cu128, built with CUDA 12.8. This indicates a high-performance computing setup optimized for GPU acceleration. PyTorch is a crucial component in the AI ecosystem, providing the framework for training and deploying neural networks. The specific version and CUDA compatibility ensure that the system can leverage the GPU resources effectively. This is particularly important for vLLM, which relies heavily on GPU acceleration to achieve high throughput and low latency. The PyTorch environment is configured to maximize performance, utilizing the available CUDA cores for parallel processing. This setup is essential for handling the computational demands of large language models like GPT-OSS. The use of PyTorch with CUDA 12.8 enables the system to harness the power of NVIDIA GPUs, significantly accelerating the model deployment and inference processes.

Python Environment

The Python version in use is 3.12.3, running on a 64-bit platform. This ensures compatibility with the various libraries and dependencies required for vLLM and GPT-OSS. Python serves as the primary language for AI development and deployment, providing a versatile and comprehensive ecosystem. The choice of Python 3.12.3 offers the latest features and improvements, ensuring a modern and efficient development environment. A stable and well-configured Python environment is crucial for the smooth operation of vLLM and the deployed models. This includes managing dependencies, resolving conflicts, and optimizing performance. The Python environment is set up to provide a seamless experience for deploying and running AI models, with all necessary tools and libraries readily available.

CUDA / GPU Information

The system is equipped with 8 NVIDIA H100 80GB HBM3 GPUs, each offering substantial memory and processing power. This massive GPU capacity underscores the system's capability to handle large models and high workloads. NVIDIA H100 GPUs are state-of-the-art accelerators designed for AI and high-performance computing tasks. The presence of 8 such GPUs indicates a significant investment in computational resources, highlighting the importance of deploying models efficiently. The GPUs are connected and configured to work in parallel, maximizing throughput and minimizing latency. This GPU-rich environment is ideal for vLLM, which is designed to take full advantage of GPU acceleration. The NVIDIA driver version is 535.216.03, ensuring compatibility and optimal performance with the GPUs. The cuDNN version, however, could not be collected, which may warrant further investigation. Overall, the GPU setup is robust and well-suited for deploying large language models.

CPU Information

The CPU is an Intel(R) Xeon(R) Platinum 8468, featuring 160 cores and a wide range of advanced instruction sets. This powerful CPU complements the GPU resources, providing additional computational capabilities for various tasks. The CPU's architecture and features, such as AVX-512 and AVX_VNNI, enable efficient processing of AI workloads. The presence of 160 cores allows for extensive parallel processing, which is beneficial for tasks that cannot be fully offloaded to the GPUs. The CPU information provides a comprehensive overview of the system's processing capabilities, highlighting its suitability for demanding AI deployments. The CPU's virtualization support (VT-x) and security mitigations further enhance the system's reliability and security. The detailed CPU specifications ensure that the system can handle a wide range of computational tasks, from data preprocessing to model inference.

Versions of Relevant Libraries

Key libraries include numpy (2.2.6), several NVIDIA CUDA libraries, PyTorch (2.9.0.dev20250804+cu128), transformers (4.55.2), and triton (3.4.0+git663e04e8). These libraries form the foundation of the AI development environment, providing essential tools and functionalities. The specific versions of these libraries are crucial for ensuring compatibility and stability. NumPy provides numerical computing capabilities, while the NVIDIA CUDA libraries enable GPU acceleration. PyTorch is the core deep learning framework, and transformers facilitate the use of pre-trained models. Triton is used for high-performance inference, making it a key component of vLLM. The library versions listed provide a snapshot of the software environment, highlighting the tools and dependencies used for model deployment. Ensuring that these libraries are correctly installed and configured is essential for the smooth operation of vLLM and the deployed models.

vLLM Information

The vLLM version is 0.10.2.dev2+gf5635d62e.d20250807, built with CUDA. vLLM is designed to accelerate the deployment and inference of large language models, leveraging GPU resources for optimal performance. The version information indicates a development build, which may include the latest features and bug fixes but may also be less stable than a release version. The build flags show that CUDA is enabled, but ROCm and Neuron are disabled, indicating a focus on NVIDIA GPUs. The GPU topology provides detailed information about the connections between the GPUs, which is crucial for optimizing data transfer and parallel processing. The NUMA configuration and CPU affinity settings are also important for ensuring efficient resource allocation. The vLLM information provides a comprehensive overview of the deployment environment, highlighting the software and hardware configurations that influence performance.

Environment Variables

The environment variables NCCL_CUMEM_ENABLE=0 and PYTORCH_NVML_BASED_CUDA_CHECK=1 are set, along with TORCHINDUCTOR_COMPILE_THREADS=1. These variables influence the behavior of PyTorch and CUDA, potentially affecting performance and resource utilization. Environment variables play a critical role in configuring the runtime environment for AI applications. NCCL_CUMEM_ENABLE controls the use of CUDA memory management, while PYTORCH_NVML_BASED_CUDA_CHECK enables PyTorch to use NVML for CUDA device checks. TORCHINDUCTOR_COMPILE_THREADS limits the number of threads used for compiling TorchInductor kernels. Understanding the impact of these variables is essential for optimizing performance and troubleshooting issues. The environment variables listed provide insights into the system's configuration and the choices made to optimize performance for AI workloads.

🐛 Bug Description: Fine-tuned GPT-OSS Model Deployment Issue

The core issue is that the fine-tuned gpt-oss model fails to deploy with vLLM using the command vllm serve ValiantLabs/gpt-oss-20b-ShiningValiant3. This indicates a potential compatibility problem between the model and the vLLM framework. Deploying fine-tuned models can be challenging due to various factors, including model format, dependencies, and hardware requirements. The error suggests that vLLM is unable to properly load or initialize the model, leading to deployment failure. Understanding the root cause of this issue is crucial for ensuring successful deployment. This section will delve into the details of the bug, examining the error message and the steps taken to reproduce the issue. By thoroughly documenting the problem, we can identify potential solutions and prevent similar issues in the future. The deployment failure highlights the importance of testing and validating models in the target environment before deploying them in production.

Installation Steps for vLLM

vLLM was installed using the following commands:

uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install --pre vllm==0.10.1+gptoss \
    --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
    --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
    --index-strategy unsafe-best-match

These commands create a virtual environment, activate it, and then install vLLM along with necessary dependencies. The use of uv pip indicates a preference for a modern package manager. Proper installation of vLLM is essential for its functionality. The --pre flag suggests that a pre-release version is being installed, which may include experimental features or bug fixes. The --extra-index-url options specify additional repositories to search for packages, including the vLLM GPT-OSS wheels and PyTorch nightly builds. The --index-strategy unsafe-best-match option allows pip to choose the best-matching package version, which can sometimes lead to compatibility issues. The installation steps provide a clear picture of the environment setup, highlighting the specific versions and dependencies used. Any issues during the installation process could potentially lead to deployment failures.

Before Submitting a New Issue...

The user confirms that they have searched for relevant issues and consulted the chatbot in the documentation. This demonstrates a proactive approach to troubleshooting. Thoroughly researching issues before submitting a bug report is crucial for efficient problem-solving. The user's diligence in searching for existing solutions and consulting the documentation chatbot highlights their commitment to resolving the issue independently. This step ensures that the bug report is focused and provides sufficient information for developers to understand and address the problem. By confirming these steps, the user demonstrates a responsible approach to bug reporting, contributing to the overall efficiency of the development process.

Conclusion

In conclusion, this article has meticulously detailed a bug encountered during the deployment of a fine-tuned GPT-OSS model using vLLM. We have explored the system's environment, the installation procedure, and the specific command used for deployment, highlighting the discrepancy between the expected and actual outcomes. The extensive system information provided offers a comprehensive understanding of the hardware and software configuration, which is crucial for diagnosing and resolving the issue. Troubleshooting complex deployment issues requires a systematic approach, and this article exemplifies such a methodology. By documenting every step and providing detailed information, we aim to facilitate the identification of the root cause and the implementation of effective solutions. The bug report serves as a valuable resource for both the user community and the vLLM developers, contributing to the overall stability and reliability of the framework. Further investigation and collaborative efforts are essential to resolve the compatibility issue between fine-tuned GPT-OSS models and vLLM, ensuring the seamless deployment and optimal performance of these models.