vLLM, the popular open-source large language model serving framework, has reached version 1.0, marking a significant milestone in the project's evolution. The new release emphasizes correctness and stability as core principles before implementing reinforcement learning corrections, reflecting a maturing approach to production-grade AI infrastructure. The team's philosophy prioritizes getting the fundamentals right rather than patching issues after deployment, which is particularly critical for organizations relying on vLLM for serving large language models at scale. The v1.0 release represents a shift in development priorities, with the Hugging Face team demonstrating commitment to building robust foundations for the broader AI ecosystem. By focusing on correctness-first principles before layering on complex features like reinforcement learning optimizations, vLLM establishes itself as a more reliable choice for enterprises and researchers deploying LLMs in production environments. This methodical approach addresses longstanding concerns about the stability and predictability of AI infrastructure tools.