Operating Distributed Inference Systems at Scale

Official Schedule Context

Official Description

Inference has rapidly become one of the most important infrastructure problems in modern computing.

As AI systems evolve into autonomous agents with persistent memory, tool usage, and multi-step

reasoning, traditional inference architectures struggle under growing demands for latency,

throughput, cost efficiency, and reliability. In this talk, I’ll share lessons from building large-

scale elastic compute and AI infrastructure systems powering production workloads. We’ll explore the

modern inference stack and the architectural patterns emerging to support next-generation agentic AI

systems. Topics include distributed inference architectures for large-scale AI systems, GPU

scheduling and elastic compute for inference workloads, multi-tenant inference infrastructure,

caching, batching, latency optimization strategies, reliability and fault isolation for inference

systems, observability and control loops for AI serving platforms, balancing cost, throughput, and

user experience, and why inference is becoming an infrastructure orchestration problem. Attendees

will gain practical insights into designing scalable, resilient, and cost-efficient inference

platforms for modern AI workloads.

Related YouTube Video

Deterministic Infra for Non-Deterministic AI Agents - Nishant Gupta, Meta Superintelligence Labs (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Notes

Supporting Slides

Slide Evidence