We have built the first of it's kind interactive blog for matching open-source LLMs to GPUs.

Hey everyone, If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster. Most of the "guides" out there are just static, out-of-date tables or dense walls of text. So, we published **"Which GPU Runs Which LLM"** on the AgentSwarms blog, but we engineered it completely differently. **What makes this different:** It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page. * You select the model size (8B, 32B, 70B, etc.). * You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ). * The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy. It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits *before* you spin up expensive cloud instances. It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)