mediumNewDatabricksHugging Face

Design the registry and deploy pipeline for model weights: 141 GB artifacts, versioning, distribution to a thousand nodes, integrity.

A 141 GB artifact is not a Docker image, and shipping it like one costs an hour per node and a registry that melts under a rollout. Content-addressed chunks, a manifest per version, a fan-out that turns one origin into a thousand nodes in minutes, integrity that fails closed, and promotion tied to evaluation.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Foundational

🖧 Hardware & Cluster Build-Out

The Bill of Materials for a Training ClusterA GPU cluster is not a pile of GPUs. A 512-GPU scalable unit built to NVIDIA's DGX SuperPOD B300 reference architecture needs 64 nodes, four separate networks, thousands of transceivers, storage that can absorb a checkpoint burst, a management plane, racks, power distribution and cooling equipment. Writing the list out in order is how a design becomes a purchase order, and the items people forget are the ones that hold up a deployment for weeks.

Foundational

📐 AI Systems Design

The AI Infra Design Round PlaybookThe AI infrastructure design round is 45 to 60 minutes with one prompt (design a serving platform, a training scheduler, a 10k-GPU cluster, a fine-tuning service) and one interviewer whose job is to find the edge of what you know. The candidates who pass do the same things in the same order: pin the requirements and the numbers in the first five minutes, draw the reference shape, size it with a stated chain of arithmetic, pick two deep dives, and name the failure modes before being asked. This page gives that structure with a minute-by-minute plan, the numbers to bring in your head, and the mistakes that end the round early.

Core

📐 AI Systems DesignSign in

GPU Job Scheduler DesignDesign a scheduler for a shared GPU cluster is the most common design prompt in AI infrastructure interviews, because it touches everything: queues and priorities, gang placement, topology, fairness across teams, preemption and the checkpoints that make it survivable, and the failure handling that keeps a 512-GPU job alive. This page builds the design in layers, states the data model and the scheduling loop, derives the numbers (how long a job waits, how much preemption costs, how much fragmentation wastes), and lists the trade-offs the interviewer will push on.

Advanced

📐 AI Systems Design🔒 Premium

Multi-Tenant Fine-Tuning ServiceA fine-tuning service takes a customer's dataset and a base model and returns a model, and the design problem is that many customers want this at once, cheaply, without seeing each other's data, on GPUs that must not sit idle between jobs. LoRA changes the shape: an adapter is a few hundred megabytes rather than a copy of the base, so many jobs can share a base in memory and many adapters can be served from one replica. This page designs the service end to end: the pipeline, the LoRA arithmetic that sets memory and cost, the isolation, the scheduler that packs jobs, and the serving path.

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UP NEXT ON YOUR JOURNEY

Next in this trackDesign rate limiting for an LLM API. Why tokens instead of requests, and how does a bucket work when the cost is unknown until the end?Next in this trackDesign a multi-region inference deployment: capacity per region, routing, failover, and getting the weights everywhere.Next in this trackDesign the playground behind an LLM API: streaming, sessions, quotas and abuse, for a million free-tier users.

FEDITOR'S NOTE

Scored on the delivery arithmetic (origin bandwidth versus node count), on content-addressed chunking with a manifest per version, on the fan-out design (peer-to-peer or tiered mirrors), and on integrity and lineage as part of the pipeline rather than an afterthought.

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