Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It

Posttraining & MidtrainingSession

Olive Song, Dan Fu

When: Wednesday, July 12:50 PM – 3:10 PM · 20 min
Where: Track 9San Francisco, CA · imported from ai.engineer's public schedule feed

About this session

Olive Song (RL Lead, https://www.minimax.io/) and Dan Fu (VP of Kernels, 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 (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

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.

AI Engineer World's Fair 2026 — unofficial import demo