Hugging Face Blog·· 2026-07-08
Native-speed vLLM transformers modeling backend
Native-speed vLLM transformers modeling backend
AI summary
Hugging Face announced that vLLM’s transformers modelling backend now matches or exceeds the throughput of vLLM’s handwritten native implementations across several LLM architectures. It uses torch.fx for static graph analysis and ast to rewrite source code, dynamically applying inference-related layer fusion at runtime to match custom-code performance.
Selection record
Threshold 60Official, first-handFirst 62Second 62
AdmittedSum of both 124 ≥ twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：vLLM推理后端与transformers模型集成
- Why it was chosen
- Speed comparisons with native vLLM implementations and a single-flag activation method show whether custom ports can be avoided.
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: Hugging Face Blog · huggingface.co