---
title: "All the Things We Have to Do to Satisfy Your Insatiable Need for Tokens"
category: "talks"
date: "2026-07-01"
time: "11:40am-12:00pm"
track: "Inference"
room: "Leadership 1"
speakers: ["Daniel Kim", "Michelle Nguyen"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Inference"
scheduleRoom: "Leadership 1"
scheduleLabels: ["Inference", "Leadership 1", "session", "confirmed"]
---
# All the Things We Have to Do to Satisfy Your Insatiable Need for Tokens

## Conference Context
- Date/time: 2026-07-01 · 11:40am-12:00pm
- Track/room: Inference · Leadership 1
- Speaker(s): Daniel Kim, Michelle Nguyen
- Session type/status: session · confirmed

- Track: Inference
- Room: Leadership 1
- Session type: session
- Status: confirmed

## Session Description
Every time the industry figures out how to serve tokens faster and cheaper, the appetite grows to match. Models get bigger, contexts get longer, agents start chaining thousands of calls together. The finish line keeps moving. This talk is a technical tour through everything the industry has done to keep up, led by two experts in high-performance inference. We'll start with the optimizations that made hardware work harder without changing the underlying architecture. Then we'll go up a level with techniques that work smarter across requests and across the model itself. And finally, a peek into the future with heterogeneous disaggregated inference, the architectural shift that splits prefill and decode across specialized hardware, and even more advanced forms of hardware specialization coming your way soon. Token demand is about to get a lot more insatiable. Let's see what the future has in store for us!

## Media Evidence
[From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva](https://www.youtube.com/watch?v=tzRvcTEapzo) (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

- Source video: `youtube-tzRvcTEapzo`
- Slide deck: [[youtube-tzRvcTEapzo-dense-slides|Dense Slides: From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva]] — 5 visible slide image(s); 5 HTML recreation(s).
![[assets/dense-slides/tzRvcTEapzo/slide-001.jpg]]
![[assets/dense-slides/tzRvcTEapzo/slide-002.jpg]]
![[assets/dense-slides/tzRvcTEapzo/slide-003.jpg]]
- Additional slide evidence: [[youtube-tzRvcTEapzo-slides|Slides: From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva]], [[youtube-tzRvcTEapzo-reconstructed-slides|Reconstructed Slides: From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva]]
- Slide-derived themes for `youtube-tzRvcTEapzo`: models, training, creator, tokens, downloads, former, google, researcher.

## 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
- `youtube-tzRvcTEapzo` — 5 slide-derived text signals
- Slide-derived themes for `youtube-tzRvcTEapzo`: models, training, creator, tokens, downloads, former, google, researcher.
- Evidence links for `youtube-tzRvcTEapzo`: [[youtube-tzRvcTEapzo]], [[youtube-tzRvcTEapzo-slides]], [[youtube-tzRvcTEapzo-dense-slides]], [[youtube-tzRvcTEapzo-reconstructed-slides]]

### 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
- [[daniel-kim]]
- [[michelle-nguyen]]

## Supporting Slides
- [[youtube-tzRvcTEapzo-slides]] — extracted from the related public AI Engineer video.

## Slide Evidence
- Slide-only cropped deck: [[youtube-tzRvcTEapzo-dense-slides]] (5 viable slide images).
- Related slide/OCR pages:
- [[youtube-tzRvcTEapzo-dense-slides]]
- [[youtube-tzRvcTEapzo-reconstructed-slides]]
- [[youtube-tzRvcTEapzo-slides]]
- Slide-derived terms: `microsoft`, `mixture`, `models`, `research`, `experts`, `daniel`, `daria`, `approach`, `queen`, `head`, `engineering`, `memory`, `conte`, `awws`, `graphite`, `windsurf`, `mongobb`, `mdaily`

## Attendance Visibility
No high-confidence attendance icon signal is shown for this talk. The sampled video evidence was either low confidence, source-proxy-only, or did not expose a clear audience view.

## Synthesis
### Synthesized Breakdown
# All the Things We Have to Do to Satisfy Your Insatiable Need for Tokens ## Conference Context - Date/time: 2026-07-01 · 11:40am-12:00pm - Track/room: Inference · Leadership 1 - Speaker(s): Daniel Kim, Michelle Nguyen - Session type/status: session · confirmed - Track: Inference - Room: Leadership 1 - Session type: session - Status: confirmed ## Session Description Every time the industry figures out how to serve tokens faster and cheaper, the appetite grows to match. Models get bigger, contexts get longer, agents start chaining thousands of calls together. The finish line keeps moving. This talk is a technical tour through everything the industry has done to keep up, led by two experts in high-performance inference.

### Speaker And Company Context
- [[daniel-kim|Daniel Kim]] — Head of Growth at [[cerebras|Cerebras]].
- [[michelle-nguyen|Michelle Nguyen]] — Co-Founder at [[gimlet-labs|Gimlet Labs]].

### Topics Covered
- [[agentic-search]]
- [[coding-agents]]

### Derived Links And Source Material
- [[youtube-tzRvcTEapzo]] — related YouTube source page.
- [[youtube-tzRvcTEapzo-slides]] — slide evidence.
- [[youtube-tzRvcTEapzo-reconstructed-slides]] — slide evidence.
- [[youtube-tzRvcTEapzo-dense-slides]] — slide evidence.

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### 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.