Hugging Face: vLLM Transformers Backend Matches or Beats Native Speed
ben_burtenshaw · x · 2026-08-04
Hugging Face announced that the transformers modeling backend for vLLM is now as fast as or faster than custom, hand-written vLLM implementations for many LLM architectures.
- Core Value: Model authors can now automatically leverage their transformers implementations to get ultra-fast vLLM inference for free, eliminating the need for manual porting.
- Benchmarks: The team tested the backend against vLLM's native implementations across three diverse Qwen3 models: a 4B dense model on a single GPU, a 32B dense model using tensor parallelism, and a 235B FP8 MoE model on an 8×H100 node. The transformers backend met or beat native throughput across all configurations.
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