System Design - Inference API

The Inference API system design problem is a hallmark of the Anthropic interview process, specifically for Software Engineering (Inference/Infrastructure) and ML Engineering roles. Unlike standard "LeetCode" problems, this question is highly practical, open-ended, and mirrors the actual challenges Anthropic engineers solve for models like Claude. YouTube +1 0 4 8.) 9

The Problem Statement

You are tasked with designing a highly scalable, low-latency, and reliable synchronous Inference API that serves Large Language Models (LLMs) to multiple product teams. PracHub 2%2C%20consistency%2C%20and%20TTLs.)

Core Requirements

• Low Latency & High Throughput: Define and meet strict Service Level Objectives (SLOs), typically p50/p95 latency targets for real-time applications like ranking or fraud detection.
• Dynamic Batching: Implement a system to group individual incoming requests into a single batch for GPU processing to maximize throughput without violating latency constraints.
• Multi-tenancy: Support multiple "tenants" (internal teams or external users) with fair-share scheduling, rate limiting, and isolated resource quotas.
• Model Versioning: Enable safe rollouts of new model versions through canarying, A/B testing, and traffic shadowing.
• GPU Memory Management: Efficiently manage GPU memory for model weights and the KV (Key-Value) cache to minimize cold starts and optimize performance. PracHub +3

The "Crucial" Complexity: Sync-to-Async Mapping

A defining feature of this interview question is handling the mismatch between the user experience and the back-end processing. The user submits a request synchronously (waiting for a result), but the backend processes it asynchronously via a batching queue. You must explain how to route the final model output back to the correct waiting user in a high-concurrency environment. Exponent 4 11

Key Technical Components to Discuss

Component | Primary Function in Inference API --- | --- API Gateway | Handles authentication, rate limiting, and initial request routing. Dynamic Batcher | The "brain" that monitors incoming traffic and decides when to trigger a GPU inference pass based on batch size or timeout. Model Registry | Stores model weights and metadata; critical for handling rollouts and versioning. GPU Workers | Stateless compute nodes that pull batches and run the actual inference. KV Cache Store | Strategies for caching intermediate model states to speed up token generation.

Interview Tips for Success

• Drive the Conversation: Anthropic interviewers often give a minimal prompt and wait. You are expected to proactively propose a scope, state your assumptions, and drive the design forward.
• Trade-off Focus: There is rarely a "single correct" architecture. The goal is to see you weigh trade-offs—e.g., choosing higher latency for better throughput (larger batches) vs. immediate processing for real-time needs.
• Operational Depth: Be prepared to discuss "what happens when things go wrong," including backpressure strategies, circuit breakers, and partial failures within a batch. Reddit +3

Would you like me to walk through a sample architectural solution for the dynamic batching component?

[0] - Anthropic Software Engineer Interview Process and Questions [1] - Anthropic SWE interview loop, full breakdown of all 5 rounds - Reddit [2] - Design a low-latency ML inference API - PracHub [3] - Design a batch inference API | Anthropic Interview Question [4] - Anthropic System Design Interview (2026 Guide) - Exponent [5] - The Actual Concurrency Questions from My 2025 Anthropic ... [6] - Design a GPU inference API | Anthropic Interview Question - PracHub [7] - Review an inference API design for scale - PracHub [8] - Anthropic Staff Success Story - Hello Interview [9] - How to answer "Why Anthropic?" interview question (+ example) [10] - AI Interview Mastery Series Day 5 — Scaling the Machine: Infrastructure Blueprints for Low‑Latency… [11] - Anthropic System Design Interview (2026 Guide) [12] - Build a Story App on Amazon Bedrock: Hands-On with InvokeModel API