Geek Out Time: RouteLLM with Nvidia NIM, A Cost-Effective Solution for Integrating Multiple Models?

In the past few Geek Out Time sessions, I have explored Nvidia NIMS and Microsoft Autogen. It’s been fascinating to see how MS Autogen can leverage various local and online models. Another intriguing aspect is the challenge of reducing inferencing costs. Today, we’re going to play with an open-source library called RouteLLM, which promises to address these issues by integrating and managing different AI models efficiently.

Install RouteLLM

To start, we need to install RouteLLM on your machine: ( https://github.com/lm-sys/RouteLLM )

git clone https://github.com/lm-sys/RouteLLM.git cd RouteLLM pip install -e .[serve, eval ] Setting Up the Environment Then we must set up our environment with the necessary API keys for Nvidia NIM and OpenAI. I created the file run_route_llm.py under the “examples” folder.

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