gpu
FDE interview questions tagged gpu, across every topic.
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Concepts behind "gpu"
The curriculum that explains the ideas these questions test.
Core
GPU Memory and VRAMVRAM is the budget that decides which models you can actually run. It is spent on three things: model weights, the KV cache, and activations. Knowing the back-of-envelope arithmetic (a 7B model at fp16 is roughly 14GB of weights) is what separates a candidate who has deployed an LLM from one who has only read about it.🖥️ ML Infrastructure & ServingSign in
Core
GPU Architecture and ExecutionA GPU is not a fast CPU. It runs thousands of threads in lockstep groups called warps across many streaming multiprocessors, under the SIMT model, and its real constraint is moving data through a memory hierarchy that spans fast on-chip registers and shared memory down to slow off-chip HBM. Understanding occupancy, coalesced memory access, and warp divergence is what separates a kernel that hits peak throughput from one that leaves 90% of the chip idle.🖥️ ML Infrastructure & ServingSign in