TL;DR We integrated Helion into vLLM’s linear backend to explore how an autotuned, high-level kernel DSL can improve LLM inference performance while reducing kernel implementation complexity. A single Helion general...
TL;DR Taking place October 20 to 21 in San Jose, California, PyTorch Conference North America 2026 highlights open research, tooling, and performance optimization across compiler architecture, cross-hardware kernel domain-specific languages,...
TL;DR The HuggingFace Kernels project now has Helion support. This blog walks through how to build, autotune, and ship performant and portable Helion kernels via the Hugging Face Kernels project,...
TL;DR Helion is PyTorch’s high-level DSL for writing performance-portable ML kernels. Partnering with Google, we have built a TPU backend that compiles Helion kernels to Pallas, providing a PyTorch-friendly way...
TL;DR In April 2025, the PyTorch Foundation evolved into a multi-project Foundation, with the objective to support deeper collaboration across domains and help scale innovation throughout the AI lifecycle. Today,...
TL;DR In this blog post, we present various novel kernel fusion techniques for common normalization ops like LayerNorm and RMSNorm, which provide significant speedup by reducing the memory-IO overhead of...
TL;DR Helion, PyTorch’s domain-specific language (DSL) for performance portable machine learning kernels, heavily relies on autotuning for performance. Currently Helion searches utilize the Likelihood-Free Bayesian Optimization (LFBO) to find the...
TL;DR 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...
