#kernels

The Reddit thread zeroed in on a hard lesson for AI-written kernels: verifier success can miss optimizer- and data-dependent numerical failures.

Why it matters: kernel work is what decides whether long-context and edge-side agent systems stay theoretical or become cheap enough to run. Qwen says FlashQLA delivers 2-3x forward speedup and 2x backward speedup over the FLA Triton kernel on NVIDIA Hopper.

Kernel work can shift the cost curve faster than another small model launch, and Qwen is leaning into that angle. In its X post, the team claimed 2–3x forward speedups and 2x backward speedups for Hopper-based linear attention workloads, with code already live on GitHub.

The top comment went straight to the CP joke, but the post held because the technical claim was concrete: 2-3x forward speedups and 2x backward speedups for GDN chunked prefill, aimed at long-context and edge-side agentic inference.

Hugging Face is trying to turn optimized GPU code into a Hub-native artifact, removing one of the messier deployment steps for PyTorch users. Clement Delangue says the new Kernels flow ships precompiled binaries matched to a specific GPU, PyTorch build, and OS, with claimed 1.7x to 2.5x speedups over PyTorch baselines.