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Imagine four people booking a ride to the same destination. If each person takes a separate taxi, four vehicles make the same trip. But if they travel together in a bus, everyone reaches the destination in a single trip. The bus carries more people at once and makes better use of the resources.
Learn how to deploy Meta's Llama 3.1 8B Instruct model using the vLLM inference engine on Runpod Serverless for blazing-fast performance and scalable AI.
Learn how vLLM achieves higher throughput than Hugging Face Transformers by using PagedAttention to eliminate memory waste, boost inference.
Discover how to deploy Meta's Llama 3.1 using Runpod's new vLLM worker. This guide walks you through model setup, performance benefits, and step-by-step.
Learn how to run vLLM on Runpod’s serverless GPU platform. This guide walks you through fast, efficient LLM inference without complex setup.
Simple RAG example on the Oscars using Llama 3.1 open models and the Haystack LLM framework. Installation ! pip install transformers-haystack sentence-transformers-haystack bitsandbytes Authorization you need an Hugging Face account you need to accept Meta conditions here:…
Welcome to this step-by-step tutorial where we’ll build a simple Retrieval-Augmented Generation (RAG) pipeline using Haystack and OPEA. We’ll fetch the newest Hacker News posts, feed them to a lightweight LLM endpoint (OPEAGenerator), and generate concise one-sentence summaries (based on this…
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