If you are preparing for a system design interview at OpenAI or Nvidia, you face a specific challenge: almost no preparation material exists for these companies. Standard system design resources cover URL shorteners and Twitter feeds — they do not cover GPU cluster scheduling or distributed model training infrastructure.
This guide fills that gap. It covers the specific system design topics that come up at OpenAI and Nvidia, the unique dimensions these companies evaluate, and how to prepare for interview formats that most candidates have never encountered.
Why OpenAI and Nvidia Are Different
Both companies operate at the frontier of AI infrastructure, but their engineering focus differs:
OpenAI builds and operates the world's most widely used AI APIs — ChatGPT and the OpenAI API. Their infrastructure challenges are primarily about serving millions of users with low latency, managing GPU capacity at enormous scale, and building reliable AI product infrastructure.
Nvidia is the company that builds the hardware that powers all of AI. Their software engineering teams build CUDA libraries, distributed computing frameworks, and the software that makes GPU clusters programmable and efficient. Engineering interviews at Nvidia skew heavily toward systems programming, GPU architecture, and performance optimisation.
Both companies sit at the center of the AI-first generation of elite technology companies — alongside Anthropic, Google, Meta, and SpaceX — and both maintain extremely high engineering bars.
OpenAI System Design Interview: What to Expect
The Problem Types
OpenAI system design questions tend to fall into two categories:
AI serving infrastructure. How do you serve a large language model to millions of concurrent users? How do you design a system that handles requests with wildly different input lengths and output lengths? How do you manage GPU capacity dynamically as demand fluctuates?
AI product infrastructure. How would you design the data pipeline that collects conversation data for model fine-tuning? How would you build the experiment tracking system that allows researchers to compare model performance across training runs? How would you design a system that enforces usage limits fairly across millions of API users?
Scale at OpenAI
OpenAI serves hundreds of millions of users. When answering system design questions at OpenAI, you should be thinking about:
- Tens of thousands of GPU servers in multiple data centres
- Millions of API requests per hour across different models (GPT-4, o1, Whisper, DALL-E)
- Global users requiring low-latency responses across multiple regions
- Continuous model updates that must be deployed without downtime
Candidates who design for startup-scale systems will not pass senior-level interviews at OpenAI.
Key Topic: GPU Capacity Planning and Scheduling
This is perhaps the most unique system design topic you will encounter at OpenAI. The question might be: "How would you design a system to schedule and allocate GPU capacity across different models and user tiers?"
Key concepts to understand:
Preemption. GPU jobs can be interrupted and resumed. A lower-priority training job may need to yield GPU resources to handle a spike in real-time inference requests. How does the scheduler make this decision, and how does it minimise the cost of interruption?
Bin packing. Different models and requests have different GPU memory requirements. Efficiently packing requests onto available GPU capacity (without wasting VRAM) is a combinatorial optimisation problem with real operational consequences.
Multi-tenancy. Enterprise customers, consumer users, and internal research teams all share the same GPU infrastructure. The scheduler must enforce fairness and priority rules across all of these while maximising overall utilisation.
Model deployment. When a new model version is ready, how do you deploy it to production without taking any serving capacity offline? This requires careful orchestration of model weight loading, health checking, and traffic shifting.
Key Topic: Vector Database Design
With the rise of RAG (Retrieval-Augmented Generation), vector databases have become critical AI infrastructure. An OpenAI system design question might be: "Design a vector database that can serve billions of embeddings with sub-100ms P99 latency."
Key concepts:
- Approximate nearest neighbour (ANN) algorithms. HNSW (Hierarchical Navigable Small World) graphs and FAISS are the standard approaches. How does HNSW trade off accuracy for speed?
- Sharding strategy. How do you partition a 1-billion-vector index across multiple nodes without losing the ability to do global nearest-neighbour search?
- Hybrid search. How do you combine vector similarity search with metadata filtering (e.g., "find the 10 most similar documents written after 2024 by Author X")?
- Index updates. ANN indexes are expensive to rebuild. How do you handle real-time inserts and deletes without full index rebuilds?
Nvidia System Design Interview: What to Expect
Nvidia's engineering interviews are distinct from other AI-first companies. The emphasis is on performance, efficiency, and deep systems knowledge.
The Problem Types at Nvidia
CUDA and GPU programming challenges. For roles involving CUDA, expect questions about memory coalescing, warp divergence, shared memory usage, and kernel optimisation. This is lower-level than typical system design.
Distributed computing frameworks. How would you design a collective communication library that allows GPU clusters to perform all-reduce operations efficiently? (This is directly relevant to NCCL, Nvidia's production answer to this problem.)
Inference optimisation systems. How would you design a compiler optimisation pipeline that takes a neural network description and generates efficient GPU kernels? (This is the domain of TensorRT, Nvidia's inference optimisation toolkit.)
Infrastructure for GPU clusters. How would you design the networking fabric and job scheduling system for a 10,000-GPU training cluster? This requires understanding InfiniBand, NVLink, RDMA, and the specific communication patterns of distributed training.
Key Topic: Distributed Training Infrastructure
Designing infrastructure for distributed model training is a unique system design problem. The question might be: "Design the infrastructure for training a 100-billion parameter language model on a cluster of 10,000 GPUs."
Key concepts:
Data parallelism. Each GPU trains on a different batch of data but holds a full copy of the model. Gradients are synchronised across all GPUs using all-reduce operations after each step. Works well when the model fits on one GPU.
Tensor parallelism. Individual weight matrices are split across GPUs. Requires high-bandwidth inter-GPU communication (NVLink within a node). Used when a single model layer exceeds GPU memory.
Pipeline parallelism. Consecutive model layers are assigned to different GPUs, forming a pipeline. Reduces per-GPU memory requirements but introduces pipeline bubbles that reduce efficiency.
Gradient checkpointing. A memory-saving technique that recomputes activations during the backward pass rather than storing them. Trades compute for memory.
Checkpoint management. Training a large model takes weeks. How do you checkpoint model state such that a hardware failure causes at most a few minutes of lost training time?
Key Topic: Communication Collectives
Distributed training requires frequent synchronisation of gradients across GPUs. The communication pattern is an all-reduce: every GPU must sum its local gradient updates and receive the global sum. At 10,000 GPUs, naive all-reduce is catastrophically slow.
The solution used in practice is ring all-reduce: GPUs are arranged in a logical ring, and gradient chunks are passed around the ring in two phases (scatter-reduce, then all-gather). Communication cost scales with the number of elements, not the number of GPUs.
At Nvidia, understanding ring all-reduce, tree all-reduce, and when to use each is foundational knowledge for systems roles.
How to Prepare for OpenAI and Nvidia Interviews
1. Study the Systems You Would Build
For OpenAI: understand vLLM, PagedAttention, and the architecture of production LLM serving systems. For Nvidia: understand NCCL, the CUDA memory hierarchy, and how distributed training frameworks like PyTorch DDP work.
2. Understand GPU Memory Architecture
Both companies evaluate whether you understand the constraints imposed by GPU hardware. Know the difference between HBM (high-bandwidth memory on the GPU), L1/L2 cache, and shared memory. Know why memory bandwidth is often the bottleneck in LLM inference.
3. Practice Estimation at Scale
At both companies, the expected scale is large enough that estimation skill matters. Practice calculating: how much memory does a 70B parameter model require? How many GPU-hours does it take to train on 1 trillion tokens? How much network bandwidth does a ring all-reduce require for a specific gradient size?
4. Build the Clarifying Question Habit
Despite the specialised content, the fundamental interview dynamic is the same as any system design interview: clarify requirements before designing. At OpenAI and Nvidia, constraints like GPU memory budget, latency SLA, and cluster topology are particularly important to establish early.
Frequently Asked Questions
Do I need to know GPU programming to interview at OpenAI? For most engineering roles at OpenAI (infrastructure, backend, platform), no. You need to understand GPU constraints at a systems level, not at the CUDA level. For research engineering roles, GPU programming knowledge is an advantage.
What programming languages are used at OpenAI and Nvidia? OpenAI primarily uses Python and C++. Nvidia uses C++, CUDA, and Python. For interview coding rounds, you typically code in your language of choice.
How does the interview process at these companies compare to FAANG? Both processes are longer and more specialised than typical FAANG interviews. Expect 5-7 rounds. System design rounds are weighted more heavily, and domain-specific technical depth is evaluated explicitly.
What is the single most important topic to study for OpenAI system design? LLM inference infrastructure — specifically KV cache design, request batching strategies, and streaming token generation. This underlies nearly everything OpenAI builds and is almost certain to appear in some form.
Practice these questions
Reading about AI infrastructure design is not the same as being asked to do it under follow-up pressure. These interactive walkthroughs cover the topics above directly:
- Design an LLM Inference Platform — batching, KV cache, streaming, and GPU utilisation, end to end
- Implement a KV Cache for Transformer Inference — the coding version of the idea that underpins the whole serving stack
- Implement a Continuous Batching Scheduler — why static batching wastes a GPU and what replaces it
- Implement Speculative Decoding — the standard follow-up when you claim you can cut latency
- Design a Distributed Model Training Platform — the training-side counterpart, and where Nvidia-flavoured questions concentrate
If you are targeting a specific company, the OpenAI and Nvidia prep pages collect the questions and topics each loop leans on.