InfoQ: The Truth About RAG & vLLM: Why Your Multimodal System Fails at Scale

The RAG hype train is off the rails until you nail the nitty-gritty: picking the right embedding model, mastering vector indexes (FLAT vs. IVF vs. HNSW), and blending BM25 with similarity search and metadata filters. Skip these and your Retrieval-Augmented Generation or Pixtral+vLLM stack will choke on scale, speed, and accuracy.

On the inference side, it’s all about smart batching, tensor parallelism, quantization, and paged attention to tame latency and throughput. Stephen Batifol even walks through a live Pixtral–Milvus–vLLM setup and spills the secrets to proper evaluations—no more “vibe checking” your RAG in production!

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