FareedKhan-dev/llm-scale-deploy-guide
An end-to-end pipeline to optimize and host LLM for 100K parallel queries
This guide helps developers who are building applications that use Large Language Models (LLMs) and need them to respond quickly and handle many user requests at the same time. It shows how to take an LLM, optimize its performance and memory usage, and then deploy it so it can serve hundreds of thousands of parallel queries efficiently. The result is a highly scalable LLM API that can power agents, RAG bots, and other LLM-driven applications.
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Use this if you are a developer building LLM-powered applications and need to host your own LLM to serve a very high volume of parallel queries with low latency and efficient resource use.
Not ideal if you are using an existing managed LLM API and do not need to host or optimize your own models for extreme scalability.
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Jupyter Notebook
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MIT
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Last pushed
Jul 06, 2025
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