Design GPU inference request batching
Company: Anthropic
Role: Software Engineer
Category: ML System Design
Difficulty: none
Interview Round: Onsite
Design a system that serves online model-inference requests on GPUs. Requests arrive one at a time from clients, but GPU throughput is much better when compatible requests are grouped into batches.
Discuss how you would design a service that:
- accepts low-latency inference requests,
- batches compatible requests together,
- routes work to GPU workers,
- supports multiple models or model versions,
- balances throughput against latency SLOs,
- handles overload, failures, and observability.
Your design should cover the API, queueing model, batching strategy, scheduling policy, worker lifecycle, autoscaling signals, and the main trade-offs.
Quick Answer: This question evaluates understanding of GPU inference batching, request queuing and routing, scheduling and autoscaling, throughput–latency trade-offs, multi-model/version management, failure handling, and observability within machine learning serving systems, and is in the ML System Design domain.