Top 50 gpu hardware Interview Questions
The most frequently asked gpu hardware questions across all roles and companies, ranked by real interview frequency. Updated daily.
Design a low-latency, cost-aware serving platform for multiple fine-tuned LLMs under variable traffic.
Grafana Labs
Sandia National LaboratoriesEExpress PortablesExplain a distributed training stack that uses GPUDirect RDMA to reduce communication overhead and improve multi node training throughput.
Together Ai
NVIDIAExplain GANs, select suitable variants, and design their training, inference, evaluation, and monitoring workflow.
InfosysImplement LoRA adapters for parameter-efficient LLM fine-tuning and explain training, serving, evaluation, and failure handling.
AppleDetermine whether model inference is memory bound or compute bound, then choose profiling evidence and optimizations.
AppleExplain cache coherence, systolic arrays, and memory architecture tradeoffs affecting ML system performance.
QualcommDesign a stable, efficient LLM training setup and choose between FP16, BF16, and FP32.
AdobeDesign a GPU attention serving path that avoids HBM materialization and supports low-latency inference at scale.
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