Standard Kernel Co.

In collaboration with CoreWeave, we show how our autonomous system optimizes LLM inference end to end, from engine configuration to custom GPU kernels: up to 63% more traffic for GLM-5.3, 2x per-user throughput for Qwen3.8-27B, and 10% more throughput for DeepSeek V4.1 Flash. 🔗

good to see we're all innovating 🫡

If AI can generate CUDA kernels, what does a kernel engineer do now?

The work is shifting from hand-writing every kernel toward building the systems that make AI-generated kernels useful in practice — helping agents learn new hardware, improving search and feedback, and building better ways to verify correctness. Generation is getting cheaper, but correctness is becoming a critical problem. A generated kernel can look faster on a benchmark but fail in production because of race conditions, stream interactions, output reuse, or other subtle bugs. That makes verification and deep hardware expertise more central to the job.

At Standard Kernel, we’re building that infrastructure. Our cofounder @anneouyang spoke with Business Insider about how kernel engineering is changing.

businessinsider.com/cuda-engineers…

We built a system combining program analysis and LLMs to transform and optimize PTX. By operating at this shared layer across DSLs (e.g. Triton, TileLang, ThunderKittens, CUTLASS), our system learns the best ideas from each and generates kernels that outperform all of them (1/5)

GPU timing is deceptively hard. Power limits, thermal state, clock behavior, caching, and measurement method all affect results in subtle ways. We explored sources of timing variation to obtain more reliable results for kernel benchmarking. (1/9)