Helion, PyTorch's kernel DSL, is showing what autotuned high-level kernels can do for production inference. In this post, we integrate Helion into @vllm_project linear backend and show how a single Helion GEMM implementation can cover multiple algorithmic variants — Standard GEMM, Split-K, and Swap-AB — with the best variant and config selected automatically per shape. On @nvidia Hopper GPUs, combining per-shape tuning with hybrid dispatch outperforms vLLM's default CUTLASS and DeepGEMM backends across the evaluated models, with consistent end-to-end gains and more than 10% throughput improvement on some workloads. Read our latest blog: ✍️ Sean Chen (@RedHat) and Shangdi Yu (PyTorch, @Meta Platforms)