cuda

The 'works on my GPU box, CUDA error 803 in prod' bug, solved at the source. What the host driver actually pins, why the toolkit version is fungible, and the multi-stage build that ships a lean reproducible image.

The NVIDIA warm-up that exposes whether you've written a kernel or just called .cuda(). The mapping from software hierarchy to silicon explains every GPU performance conversation that follows.

Kernel optimization is mostly the art of feeding 100+ TFLOPs from the small fast end of this hierarchy. Know the rough latencies and bandwidths, interviewers listen for the numbers.

A staple NVIDIA screen with a precise mechanical answer, and a follow-up trap about which branches actually diverge that catches most candidates.

The two access-pattern killers behind most 'my kernel is 10x slower than cuBLAS' mysteries. The matrix transpose walks straight into both, which is why NVIDIA loves asking about it.

Everyone says 'maximize occupancy.' The candidates who get hired at NVIDIA know when 25% occupancy beats 75%, and can explain the resource math that decides it.

Constrained JSON/grammar decoding usually means a Python callback per token, which serializes the GPU. The staff answer keeps the DFA on-device and turns the mask into a single coalesced gather.