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

Speakers: Olive Song (MiniMax, RL Lead), Dan Fu (Together AI, VP of Kernels)

• Why: Agentic workloads — prefill-heavy traffic, high cache hit rates, long-context inputs — break inference engines built for chat, and this pairs the model team and the serving-infrastructure team on the same open model family, showing both sides of the contract.
• What: Two halves: the model side (the RL decisions behind MiniMax's long-context reasoning and tool use, and what training for agentic behavior looks like in practice) and the infrastructure side (why agentic workloads break chat-era engines, and the kernel-level optimizations built for that workload profile), plus how the two teams collaborate on launches and ongoing performance work (from abstract only).
• How: The abstract stays at the workload-characterization level — prefill-heavy traffic, high cache hit rates, long-context inputs — without naming specific kernel techniques, so the depth will be in the talk.
• Where applicable: MiniMax/Together-specific production experience, but the workload-shape characterization of agentic traffic versus chat traffic is directly reusable by anyone capacity-planning an inference fleet.