MiniMax's Open Weight Model Strategy and Inference…

Terms in this piece · Glossary

inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.

Why it matters

Offers hard-won, specific engineering lessons — like the collapse pitfall and RL-against-real-environments approach — that can save practitioners from repeating the same mistakes when building or agentic/multimodal models.

Key quotes

“it aligns with our mission that we want to have intelligence with everyone”
Olive (MiniMax)

“it was trained multimodal from scratch. So, from step zero, we trained not only text data, we also trained image data.”
Olive (MiniMax)

“If someone tells me you can't do the thousand first thing, yeah, like I don't know, try harder.”
Dan (Together AI)

“It's like the type of thing that you should have done in your third year of undergrad or something like that, but most of us actually skipped that class, so now we're rediscovering it live in in industry.”
Dan (Together AI)

“we're we're seeing with models like M3 and GLM and Kimmy and and all those models that um the open-source frontier really can catch up”
Dan (Together AI)

Chapters

0:00Introducing the RL lead at MiniMax
1:17Why open source and open weights
3:45What builders are doing with the model
4:11A model that builds games
5:12Computer use and OS World
5:49Writing GPU kernels
6:25Parallel kernel bench

Intel

MiniMax's Open Weight Model Strategy and Inference Stack