Introduction to vLLM on AWS Trainium
In today’s competitive landscape, boosting cold-start recommendations is essential for businesses. This blog post focuses on how to leverage vLLM for scalable inference, utilizing AWS Deep Learning Containers (DLC) to simplify model packaging and deployment.
We will generate interest expansions using structured prompts. Next, we encode these expansions into embeddings and retrieve candidates with FAISS. Validation is crucial to ensure the results remain grounded. By framing the cold-start challenge as a scientific experiment, we can benchmark LLM and encoder pairings effectively. This allows for rapid iteration on recommendation metrics, ultimately demonstrating a clear return on investment for each configuration.
