#model-serving

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#multimodal-llms #vllm #llava #latency #real-time-architecture #candidate-generation #fastapi #onnx #docker
Artificial intelligence
fromInfoWorld
3 months ago

The 200ms latency: A developer's guide to real-time personalization

Meeting sub-200ms latency is essential for user engagement; architectures must decouple retrieval and heavy inference to serve personalized results at scale.
Python
fromPyImageSearch
8 months ago

The Rise of Multimodal LLMs and Efficient Serving with vLLM - PyImageSearch

Multimodal LLMs combine vision encoders and language models to enable image-plus-text reasoning, and vLLM provides efficient, scalable OpenAI-compatible serving for deployment.
Python
fromPyImageSearch
6 months ago

FastAPI Docker Deployment: Preparing ONNX AI Models for AWS Lambda - PyImageSearch

Build and containerize a FastAPI AI inference server serving an ONNX model with image preprocessing and Docker deployment, preparing for AWS Lambda serverless deployment.
fromPyImageSearch
8 months ago

Setting Up LLaVA/BakLLaVA with vLLM: Backend and API Integration - PyImageSearch

In this tutorial, you'll learn how to set up the vLLM inference engine to serve powerful open-source multimodal models (e.g., LLaVA) - all without needing to clone any repositories. We'll install vLLM, configure your environment, and demonstrate two core workflows: offline inference and OpenAI-compatible API testing. By the end of this lesson, you'll have a blazing-fast, production-ready backend that can easily integrate with frontend tools such as Streamlit or your custom applications.
Python
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