vLLM Just Matched DeepInfer's AI Performance?!

Want to make money and save time with AI? Join here: https://www.skool.com/ai-profit-lab-7462/about Video notes + links to the tools 👉 https://www.skool.com/ai-profit-lab-7462/about Get a FREE AI Course + Community + 1,000 AI Agents 👉https://www.skool.com/ai-seo-with-julian-goldie-1553/about Free GEO & AI SEO Checklist! https://orders.goldie.agency/signup Get a FREE GEO + AEO Strategy Session → https://go.juliangoldie.com/strategy-session?utm=julian Get 200+ Free AI SEO Prompts → https://go.juliangoldie.com/chat-gpt-prompts?utm=julian Get our free SEO Link Building Book here: https://go.juliangoldie.com/opt-in?utm=julian DeepSeek built DeepInfer, a custom engine for its own model — then open-source vLLM came within 6% of its performance. Here’s what both tools actually do, how vLLM got so close (shared roots, wide expert parallelism, and clever load balancing), and the catch in the fine print that changes how you should read every one of these numbers. 00:00 Intro – Can open source match the maker? 00:33 What Is vLLM – The engine that runs the model 00:57 What Is DeepInfer – DeepSeek's own engine 01:26 The Headline – vLLM comes within 6% 01:54 How It Got Close – Shared roots & kernels 03:23 What Is MoE – Mixture of experts explained 03:40 Wide EP – Splitting experts across GPUs 04:08 Load Balancing – Fixing overworked GPUs 04:48 The Hardware – GB200, GB300 & the big rack 05:20 Where vLLM Pulls Ahead – Online serving 06:35 The Catch – Why it's not a fair fight 07:32 So Which Is Better? – My honest take