what is minimax-m3-tp3-rtx6000 fr?

This project is a guide and set of scripts for running a very large AI language model called MiniMax-M3 on three powerful graphics cards (GPUs) at the same time, so the model's work is split across all three. MiniMax-M3 is a huge model, made up of many smaller expert networks that only partially activate for each request, and it also understands images, not just text. The main challenge the author solved is a math problem. When you split a model's work across 3 GPUs, every part of the model has to divide evenly by 3. The main language part of the model does, once a patch is applied, but the part that understands images has 16 internal attention heads, and 16 does not divide evenly by 3. Normally this crashes the software used to run the model, called vLLM, before it can even load. The fix is a small change to a few lines of vLLM's code. Instead of crashing when something cannot be split evenly across GPUs, the code now copies that piece, in this case the image understanding part, onto all three GPUs instead of splitting it. The main language model is still divided across the GPUs as normal. This means the setup can now handle both text and images, unlike an older workaround that only supported text. The README includes benchmark tables showing how many words per second the model produces at different amounts of remembered context, up to 240,000, and shows perfect scores on a memory test that checks whether the model can find specific information hidden in a long block of text. It also confirms that sending images to the model, through a standard chat API, works correctly. Beyond the explanation, the README gives full step by step instructions: the exact Docker command to start the container, the exact code to apply the patch, and the command to launch the model server, along with tables describing hardware requirements and memory usage per GPU.