Markdown source

Optimizing Open Models for Production Grade Inference

Conference Context

Session Description

Open-source foundation models are rapidly closing the gap with proprietary systems, enabling organizations to build powerful AI applications with greater flexibility and control. However, deploying these models in production introduces a new set of challenges: latency, throughput, scalability, and cost efficiency.In this talk, we'll explore the modern inference optimization techniques that power large-scale AI systems in production. Topics include KV cache optimization, cache-aware routing, prefill/decode disaggregation, speculative decoding, and other emerging approaches used to improve performance and reduce infrastructure costs.Through practical examples and real-world architecture patterns, attendees will gain a deeper understanding of how to run open models efficiently at scale.

Media Evidence

No related AI Engineer channel video found yet.

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

No linked video, transcript, or slide source has been attached yet.

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

People

Notes

Synthesis

Synthesized Breakdown

Optimizing Open Models for Production Grade Inference ## Conference Context - Date/time: 2026-07-01 · 2:25pm-2:45pm - Track/room: track TBD · Expo Stage 1 NE - Speaker(s): Sujee Maniyam, Dylan Bristot - Session type/status: session · confirmed - Track: track TBD - Room: Expo Stage 1 NE - Session type: session - Status: confirmed ## Session Description Open-source foundation models are rapidly closing the gap with proprietary systems, enabling organizations to build powerful AI applications with greater flexibility and control. However, deploying these models in production introduces a new set of challenges: latency, throughput, scalability, and cost efficiency.In this talk, we'll explore the modern inference optimization techniques that power large-scale AI systems in production. Topics include KV cache optimization, cache-aware routing, prefill/decode disaggregation, speculative decoding, and other emerging approaches used to improve performance and reduce infrastructure costs.Through practical examples and real-world architecture patterns, attendees will gain a deeper understanding of how to run open models efficiently at scale. ## Media Evidence No related AI Engineer channel video found yet.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.