One Model Definition Reaches Native Serving Speed

Hugging Face's updated vLLM integration uses graph analysis and source transformation to recognize model patterns, fuse operations, infer parallel plans, and route compatible work through optimized vLLM kernels. In the authors' tests, the Transformers backend matched or exceeded vLLM's hand-written implementations for three Qwen3 deployments ranging from a single-GPU dense model to a 235-billion-parameter mixture-of-experts system. The measurements cover selected compatible architectures, and linear-attention models remain unsupported. The engineering gain is a shorter path from one readable model implementation to high-performance serving.