Prepare for your NVIDIA MTS interview
GPU architecture, CUDA optimization, and ML systems design cases calibrated to NVIDIA’s hardware-deep, systems-first MTS culture.
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The Interview
What NVIDIA is looking for
Systems Design Interview
GPU & Inference Systems Design
01Memory Bandwidth vs Compute Bottleneck Identification
02CUDA Kernel Optimization Strategy
03Multi-GPU Parallelism Architecture
04Inference Throughput vs Latency Trade-offs
ML Systems Interview
Large-Scale ML Systems
01Distributed Training Architecture
02Quantization & Precision Trade-offs
03KV-Cache and Attention Optimization
04Speculative Decoding Pipeline Design
Behavioral Interview
Behavioral Interview
01Systems-Level Ownership
02Cross-Stack Debugging Methodology
03Performance Engineering Mindset
04Hardware-Software Co-design Thinking
Memory bandwidth vs compute bottleneck profiling on A100/H100//CUDA kernel optimization and GPU occupancy reasoning//Multi-GPU parallelism: tensor, pipeline, data parallel trade-offs
Practice Library
