Markdown source

What's New in Inference Engineering

Conference Context

Session Description

More than 30,000 engineers have learned the fundamentals of inference since Inference Engineering was published. But the field keeps accelerating, so it's time for the first public addendum to the book. The past four months have seen a renewed focus on training-dependent inference optimization across the "big three" performance techniques of speculation, caching, and quantization. This talk provides structured guidance for training DFlash and EAGLE 3 draft models to accelerate LLM decode, introduces the concept of KV compaction, and explains the hype behind TurboQuant.

Media Evidence

Optimizing inference for voice models in production - Philip Kiely, Baseten (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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

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

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

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

What's New in Inference Engineering ## Conference Context - Date/time: 2026-07-01 · 1:30pm-1:50pm - Track/room: Inference · Track 9 - Speaker(s): Philip Kiely - Session type/status: session · confirmed - Track: Inference - Room: Track 9 - Session type: session - Status: confirmed ## Session Description More than 30,000 engineers have learned the fundamentals of inference since Inference Engineering was published. But the field keeps accelerating, so it's time for the first public addendum to the book. The past four months have seen a renewed focus on training-dependent inference optimization across the "big three" performance techniques of speculation, caching, and quantization. This talk provides structured guidance for training DFlash and EAGLE 3 draft models to accelerate LLM decode, introduces the concept of KV compaction, and explains the hype behind TurboQuant.

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.