Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It
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 Speakers: Olive Song — MiniMax; Dan Fu — Together AI.
About the Posttraining & Midtraining Track
Posttraining & Midtraining sessions at AI Engineer World's Fair 2026 in San Francisco.
When
Wednesday, July 1, 2026
2:50 PM - 3:10 PM·20m
Where
Track 9 · Room 2016
Capacity: 250 attendees
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Speakers (2)
Olive Song
RL Lead
MiniMax
Researcher at MiniMax focused on reinforcement learning and model evaluation for the M-series models.
Dan Fu
VP of Kernels
Together AI
@realDanFu
VP of Kernels at Together AI and Assistant Professor of Computer Science and Engineering at UC San Diego, focused on efficient machine learning systems and GPU performance.