What AI Engineers Get Wrong When Deploying Open-Source Models to Product

This story was originally published on HackerNoon at: https://hackernoon.com/what-ai-engineers-get-wrong-when-deploying-open-source-models-to-product. Think your GPU setup is fine? These 4 sneaky mistakes in serving open-source LLMs could be costing you more than you realize. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #llm-deployment, #open-source-llms, #gpu-optimization, #model-serving, #vllm, #retrieval-augmented-generation, #ml-inference-optimization, #good-company, and more. This story was written by: @merry-n-proprietary. Learn more about this writer by checking @merry-n-proprietary's about page, and for more stories, please visit hackernoon.com. TL;DR Serving open-source models in production rarely means running a single model. It often involves many small models running on shared hardware. Fix the retrieval layer, improve GPU utilization, and do the break-even math, and the choice between frontier and open models will matter much less than you think.