The Year of the LLM GPU Kernel Engineer
Source: https://www.wafer.ai/blog/topk-sigmoid-optimization Author: Wafer Team Date: 2026-02-11
Summary
Argues that 2025-2026 is the defining year for GPU kernel engineering in LLMs. The bottleneck has shifted from model architecture to hardware utilization — and there aren’t enough engineers who can write fast CUDA kernels.
Key Claims
- The bottleneck shift: model architectures are maturing; the remaining performance gains come from hardware efficiency.
- Skill gap: GPU kernel engineering (CUDA, Triton) is rare and extremely valuable — most ML engineers can’t do it.
- Case study: optimizing a top-K sigmoid operation achieved 3x speedup for mixture-of-experts inference.
- The compounding effect: each 10% inference improvement enables 10% more experiments per dollar — compounds across the research cycle.
- Career observation: CUDA engineers are paid premiums at all top AI labs; demand is far exceeding supply.
Concepts
- RL Infrastructure — kernel efficiency enables more RL training
- Scaling & Compute — compute efficiency extends effective compute budget