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At enterprise scale, Playwright end-to-end testing succeeds or fails on one thing: whether engineers trust the suite. A hundred tests are easy. Three thousand tests running across six teams, twelve environments, and every pull request where a 2% flake rate means dozens of false failures a day is a different discipline entirely. This guide covers how to build Playwright E2E testing that stays fast, stable, and trusted as it scales: the architecture, the flaky-test controls, CI

Introduction You've finally convinced your team to self-host an LLM. You've chosen a 7B parameter model, spun up a cloud instance with two A100s, and written a basic Python script to load the model and generate text. Then reality hits: your inference server processes one request at a time, leaving 90% of your GPU compute idle. Concurrent users wait in line. Memory overflows mid-generation. And worst of all, migrating your existing OpenAI client code to hit your new server req