Native-speed vLLM transformers modeling backend

Hugging Face has introduced a native-speed backend for vLLM, the high-throughput inference engine for large language models. This integration allows developers using Hugging Face's transformers library to directly leverage vLLM's optimized serving capabilities without relying on separate deployment pipelines. Previously, vLLM required its own custom model loading and serving infrastructure, adding friction for teams already building with Hugging Face tools. The new backend aims to close that gap by making vLLM's performance benefits—such as continuous batching and PagedAttention—accessible through the familiar transformers API. For AI workflow builders, this means reduced latency and higher throughput when serving models in production, particularly for applications requiring real-time responses or handling multiple concurrent requests. The update also simplifies the path from development to deployment, as the same model can be tested locally and then pushed to a vLLM-powered serving endpoint without code changes. While still a relatively technical improvement, it removes a common pain point for teams scaling their AI applications.