Abstract: Helion kernels were integrated into vLLM for FP8 inference using Qwen3 models and evaluated across NVIDIA H100 and B200 GPUs. The experiments show that Helion provides a productive PyTorch-native workflow for developing fused GPU kernels while delivering performance improvements for many quantization, normalization, and fusion-heavy inference kernels. End-to-end benchmarks demonstrated throughput gains across multiple serving scenarios, with additional optimization work underway for GEMM performance on Blackwell GPUs.