From AI Hype to GPU Load: Scaling MiniMax H3 with Sogni × Nosana

​MiniMax H3 is gaining serious traction across both Sogni and Nosana — and that growth brings a very real scaling challenge.

​Built for high-quality image-to-video generation with native audio, MiniMax H3 is quickly becoming one of the standout models for creators and teams working with next-gen video generation.

​Join Sogni × Nosana for a practical session on what it takes to scale a fast-growing AI model without overprovisioning infrastructure, wasting GPU capacity, or letting costs spiral.

​We’ll cover:

• ​🎬 Why MiniMax H3 is seeing strong adoption on Sogni and Nosana

• ​⚡ How to scale GPU capacity as demand increases

• ​💸 How to keep infrastructure costs and resource usage under control

• ​🌐 Where decentralized GPU infrastructure fits when workloads grow

• ​🌱 How to scale generative AI more responsibly, with better compute efficiency and thoughtful model use

• ​🛠️ Lessons from running real MiniMax H3 workloads across Sogni and Nosana

​If you’re building with generative video or managing GPU-heavy AI workloads, this session is about moving from hype to real usage, and scaling it in a smarter, more responsible way.