Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It
Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It
- Day
- Day 3 — Session Day 2
- Time
- 1:30pm-1:50pm
- Room
- Track 9
- Track
- Posttraining & Midtraining
Accessible with the Engineering pass and above.
About this session
Olive Song (RL Lead, https://www.minimax.io/https://www.minimax.io/) and Dan Fu (VP of Kernels, https://www.together.ai/https://www.together.ai/) dig into the engineering behind one of the most widely used open model families in the agent ecosystem: how MiniMax built the model for agentic workloads, and what it takes to serve it at scale. Olive on the model side: The RL decisions behind long-context reasoning and tool use What training for agentic behavior actually looks like in practice Dan on the infrastructure side: Why agentic workloads break inference engines built for chat: prefill-heavy traffic, high cache hit rates, long-context inputs The kernel-level optimizations built for MiniMax's workload profile How the two teams collaborate on model launches and ongoing performance work
