GPU Optimization

GPU Optimization is the practice of writing and tuning code so that computations run as efficiently as possible on Graphics Processing Units, exploiting their massively parallel architecture. It covers topics such as CUDA kernel design, memory hierarchy management (registers, shared memory, HBM), thread-block occupancy, and hardware-specific features like Tensor Cores. Practitioners work at multiple abstraction levels—from Python-level libraries (CuPy, Triton) down to low-level PTX assembly—to squeeze maximum throughput from the hardware.

Modern AI training and inference are almost entirely bottlenecked by GPU compute, so engineers who can write fast kernels directly translate their skills into faster model iteration and lower serving costs. The rise of open-weight LLMs and the push for on-device inference have made custom kernel work a core competency at AI labs, cloud providers, and hardware startups. As GPU architectures evolve rapidly (Hopper, Blackwell, AMD MI300X), the ability to retune kernels for new hardware is a durable, high-demand skill.