C++ CuTe / CUTLASS vs CuTeDSL (Python) in 2026 — what should new GPU kernel / LLM inference engineers actually learn?[D]

For people just starting out in GPU kernel engineering or LLM inference (FlashAttention / FlashInfer / SGLang / vLLM style work), most job postings still list “C++17, CuTe, CUTLASS” as hard requirements.

At the same time NVIDIA has been pushing CuTeDSL (the Python DSL in CUTLASS 4.x) hard since late 2025 as the new recommended path for new kernels — same performance, no template metaprogramming, JIT, much faster iteration, and direct TorchInductor integration.

The shift feels real in FlashAttention-4, FlashInfer, and SGLang’s NVIDIA collab roadmap.

Question for those already working in this space:

For someone starting fresh in 2026, is it still worth going deep on legacy C++ CuTe/CUTLASS templates, or should they prioritize CuTeDSL → Triton → Mojo (and keep only light C++ for reading old code)?

Is the “new stack” (CuTeDSL + Triton + Rust/Mojo for serving) actually production-viable right now, or are the job postings correct that you still need strong C++ CUTLASS skills to get hired and ship real kernels?

Any war stories or advice on the right learning order for new kernel engineers who want to contribute to FlashInfer / SGLang / FlashAttention?

Looking for honest takes — thanks!