Gestell

Gestell fills a specific gap: analyzing the compiled PTX and SASS output of GPU kernels. Most profilers stop at runtime metrics, but Gestell shows you exactly how the compiler transformed your code. This is invaluable when you're chasing that last 10% performance on Hopper or debugging a mysterious warp stall. Pick it if you're a performance engineer optimizing CUDA/Triton kernels and need to see the lowering steps. The pull request review feature is smart — it catches regressions before they ship. The architecture comparison (Ampere vs. Hopper) is a nice touch. Skip it if you're a high-level ML practitioner using PyTorch without writing custom kernels. You'll never touch PTX, and the tool's power would be wasted. Compared to NVIDIA Nsight Compute, Gestell is narrower — Nsight does profiling, debugging, and more — but Gestell digs deeper into the compiled representation. For compiler engineers targeting GPU backends, Gestell is a better fit. A caveat: the tool is in active development, and pricing isn't public. You'll need to contact the team, which is fine for enterprises but a friction point for individual developers. In practice, we'd use Gestell during kernel development sprints, not as a daily driver. It's a scalpel, not a swiss army knife.