Vision Language Models

Chapter 7. Deploying Models for Inference at Scale

You have trained your vision language model (VLM) and now you want people to actually use it. This is where inference deployment comes in, and it is where many of us discover that “the model works in my setup” is far from “the model serves 1,000 users reliably.”

Deployment is two challenges bundled together. First, there is the optimization challenge: how do you make inference fast and cheap enough to be practical? Second, there is packaging and deploying your model where it needs to run, whether that’s a cloud cluster, a browser, an edge device, or specific hardware.

In this chapter, we tackle multiple problems: inference optimization, serving frameworks like vLLM, and how to make models portable across runtimes and deploy them. Some of the libraries mentioned here are Swiss Army knives: they export a model to their own format, quantize, and handle serving.

Learning Objective

By the end of this chapter, you will know how to take a trained VLM from notebook to production: profiling what’s actually slow, squeezing performance through quantization and attention optimizations, and serving real concurrent traffic efficiently.

Inference Optimization for VLMs

You have trained your VLM and confirmed its accuracy through your tests. Now, you’re ready to roll it out. However, when you start serving real users, a new question emerges: why so little throughput?

This section provides you with the mental models and hands-on techniques ...