Deploying gpt-oss with vLLM and BentoML

Open-source models like DeepSeek-R1 and gpt-oss allow users to self-host powerful reasoning models, offering more control and cost-efficiency compared to closed-source APIs. By using frameworks like vLLM and tools like BentoML, developers can create private inference APIs with customizable inference logic and optimized performance through techniques such as prefill–decode disaggregation. The article guides users through self-hosting gpt-oss using vLLM and BentoML, highlighting the benefits of deploying on BentoCloud, a managed inference platform with features like fast autoscaling and LLM-specific observability. vLLM, developed by UC Berkeley researchers, is noted for its high-performance capabilities, making it an ideal choice for handling large language models (LLMs). The deployment process includes setting up a virtual environment, defining model and GPU configurations, configuring runtime environments, and launching a vLLM server within a BentoML service. The article further explains how to deploy gpt-oss to BentoCloud, test OpenAI-compatible APIs, and optimize deployments with scale-to-zero capabilities, while also providing insights into the benefits of BentoML and vLLM, including their ability to efficiently handle large-scale production environments.

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