Master Cloud AI Inference: Load‑Testing Strategies with Alibaba PAI‑EAS

In today’s rapidly evolving AI landscape, large language models (LLM) and multimodal models are reshaping industries. Inference services are essential for moving models from laboratory breakthroughs to production, requiring handling of high concurrency, low latency, heterogeneous‑hardware optimization, and precise cost control.

Alibaba Cloud AI platform PAI provides a full‑stack, highly available inference service. This series explores distributed inference architecture, serverless elastic resource global scheduling, load‑testing optimization, and service observability, showcasing PAI’s capabilities for AI inference.

Building an efficient, scalable AI inference platform in the cloud must address the computational complexity of trillion‑parameter models, high concurrency, low latency, and dynamic load. Only a scientific load‑testing system can verify platform limits under real‑world traffic.

PAI‑EAS offers several testing modes—fixed concurrency, fixed request‑rate (RPS), and maximum throughput. It can simulate test data, create tasks with one click, and automatically generate core metrics such as TTFT, TPOT, TPS, ITL, E2EL, providing average, median, and P99 values for comprehensive performance evaluation.

TTFT (Time To First Token) : request‑first‑packet latency, measured from request sent to receipt of the first generated token.

TPOT (Time per Output Token) : latency per token, representing the interval between two consecutive generated tokens.

TPS (Token Per Second) : number of tokens transmitted per second.

Requests‑per‑second distribution : distribution of request counts received each second.

Response time distribution : distribution of response counts returned within a selected time window.

Transmission traffic distribution : distribution of inbound request data volume and outbound response data volume over the selected period.

Response time interval distribution : proportion of response times falling into each millisecond interval.

Overall response time distribution : end‑to‑end latency percentiles (e.g., median, P99) in milliseconds.

Status code distribution : distribution of HTTP status codes returned by the service.

How to Use PAI‑EAS

1. Log in to the PAI console at https://x.sm.cn/38G17Vo , select the target region and workspace, then enter EAS.

2. Switch to the "Load Test Tasks" tab, click "Add Load Test Task", and when creating the task, check the LLM service option to obtain a customized LLM‑specific load‑testing report.

Performance Metrics Configuration

Test Mode Configuration

View Real‑Time Monitoring Data

After a load‑test task completes, the full report can be viewed on the task detail page.

Series Overview: Mastering Cloud AI Inference Platform

This series deep dives into PAI’s technical architecture, best practices, and industry applications, covering:

Technical panorama: distributed inference, dynamic resource scheduling, and serverless foundations for trillion‑parameter models.

Practical guide: performance tuning, cost optimization, and global scheduling case studies.

Industry enablement: real‑world deployments in finance, internet, and manufacturing.

Whether you are an AI developer, architect, or enterprise decision‑maker, the series provides end‑to‑end guidance to help you seize opportunities in the AI era.