How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Anthropic.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Anthropic during technical interviews.

Design a GPU Inference API

Last updated: May 23, 2026

Quick Overview

This question evaluates a candidate's ability to design scalable GPU-backed inference APIs, testing competencies in system architecture, resource management, request lifecycle design, multi-tenancy, model versioning, and operational engineering within the ML System Design domain, and it spans both conceptual architecture and practical operational considerations. It is commonly asked to assess how applicants reason about latency-sensitive synchronous inference, independent CPU and GPU scaling, reliability, observability, capacity planning, and rollout strategies, reflecting real-world trade-offs encountered when deploying production ML services.

Anthropic

Feb 21, 2026, 12:00 AM

Software Engineer

Onsite

ML System Design

Design a scalable inference API for serving machine learning models on GPU-backed workers.

The API should support synchronous prediction requests from product services, perform CPU-side request validation and preprocessing, execute model inference on GPUs, and return low-latency responses. Assume the system must scale from a small deployment to high traffic with multiple model versions and tenants.

Discuss:

Public API shape and request lifecycle.
Core architecture and data flow.
How to scale CPU and GPU components independently.
What you would do if CPU utilization is low but GPUs are saturated.
Reliability, observability, capacity planning, and rollout strategy.

Solution

Show

Comments (0)

Loading comments...

Browse More Questions

More ML System Design•More Anthropic•More Software Engineer•Anthropic Software Engineer•Anthropic ML System Design•Software Engineer ML System Design