The vLLM inference framework has integrated Helion, a PyTorch‑native kernel DSL, into its linear backend. Helion lets a single GEMM kernel cover multiple algorithm variants and automatically picks the fastest configuration for each shape. On NVIDIA Hopper GPUs the new backend beats vLLM’s default CUTLASS and DeepGEMM kernels, delivering consistent end‑to‑end speed gains and more than a 10% boost in throughput for some models.
Why it matters
Developers running large language models on Hopper GPUs can see faster inference without writing custom kernels.
