{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Good Tech Companies ","title":"What AI Engineers Get Wrong When Deploying Open-Source Models to Product","html":"","width":"100%","height":180,"duration":477,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/what-ai-engineers-get-wrong-when-deploying-open-source-models-to-product.\nThink your GPU setup is fine? These 4 sneaky mistakes in serving open-source LLMs could be costing you more than you realize.\nCheck more stories related to undefined at: https://hackernoon.com/c/undefined.\n 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.\nThis story was written by: @merry-n-proprietary. Learn more about this writer by checking @merry-n-proprietary's about page,\n and for more stories, please visit hackernoon.com.\nTL;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.\n \n ","thumbnail_url":"https://img.transistorcdn.com/HZ9CRzf5js9DK86xzUVMWBRbXYwg4dA8xVXJGVzpL6Y/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8xMTNl/MjgwMmI0ZmEzNThj/YmJiOWNiN2UyZmRm/MzY3My5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}