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.

Socratify AI

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

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

NVIDIA exercises

Ready to ace your NVIDIA MTS interview?

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.

Socratify AI

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

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

How It Works

Master NVIDIA MTS Interviews

GPU architecture and inference optimization cases, scored on NVIDIA’s MTS evaluation dimensions. · Click to expand

Research Methodology

Practitioner

Training System Design

Practitioner

1Your Member of Technical Staff Goals

2Member of Technical Staff Case Prep

LLM inference on an H100: memory bandwidth at 68%, compute at 22%. Identify the...

LEARNHard

AI Hardware / Systems · Systems OptimizationNVIDIA

3Frameworks

Framework

Is the training data mixture causing the reasoning capability gap?

Is STEM and reasoning data underrepresented in the training mix?

Current mix: 60% web crawl, 5% STEM corpora — MMLU requires ≥20% reasoning-heavy data

STEM token count: 3B vs GPT-3.5 estimated equivalent 15B — 5× reasoning deficit

Is benchmark contamination inflating GPT-3.5's reported MMLU score?

n-gram dedup check found 2.1% overlap between MMLU test questions and our training corpus

Our model trained without decontamination — actual capability gap may be narrower than 12pp

Were training hyperparameters suboptimal for this model and compute scale?

Was the learning rate schedule misconfigured for 7B scale?

Final LR: 1e-5 — Chinchilla-optimal for 7B at this token count is 3e-5 (3× too conservative)

12% of training steps ran at >2× optimal LR due to warmup misconfiguration — gradient instability window

Was the batch size appropriately scaled with model depth and sequence length?

Batch size: 2M tokens per step — compute-optimal for 7B scale is 4M (2× under-batch)

Gradient norm spiked >1.5 in final 20% of training — correlates with batch-size mismatch

Is the evaluation methodology creating a misleading comparison?

Are we using the same eval format (few-shot vs zero-shot) as the GPT-3.5 benchmark?

Our eval: 0-shot MMLU — GPT-3.5 official results were 5-shot (+8pp average on MMLU)

Switching to 5-shot narrows gap to 4pp — 67% of apparent gap is eval format mismatch

Is the remaining gap an instruction tuning gap rather than a pretraining gap?

GPT-3.5 is instruction-tuned via RLHF; our model is base pretrain — SFT typically adds 3-5pp on MMLU

Preliminary SFT on 100K instruction examples closes residual gap to <1pp — training deficit ruled out

Ablation Study Design

Distinctive

Scaling Law Reasoning

Strong

Evaluation Methodology

Meeting Bar

Training Diagnostics

Strong

4Feedback

How It Works

Master NVIDIA MTS Interviews

GPU architecture and inference optimization cases, scored on NVIDIA’s MTS evaluation dimensions. · Click to expand

1Your Member of Technical Staff Goals

Research Methodology

Practitioner

Training System Design

Practitioner

2Member of Technical Staff Case Prep

LLM inference on an H100: memory bandwidth at 68%, compute at 22%. Identify the...

LEARNHard

NVIDIA