---
title: "Slides: From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva"
category: "slides"
video_id: "tzRvcTEapzo"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva

## Source Video
[From Mixture of Experts to Mixture of Agents with Super Fast Inference - Daniel Kim & Daria Soboleva](https://www.youtube.com/watch?v=tzRvcTEapzo)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/tzRvcTEapzo/slide-001.jpg]]

OCR text:

> INNOVATIONPARTNER
> aws
> PLATINUMSPONSORS
> Graphite
> W Windsurf
> MongoDB
> daily
> augment code
> Workos

![[assets/slides/tzRvcTEapzo/slide-002.jpg]]

OCR text:

> AIE From Mixture of Experts to Mixture of Agents
> Daniel Kim DariaSoboleva
> HeadofGrowth Head ResearchScientist
> Microsoft smol?

![[assets/slides/tzRvcTEapzo/slide-003.jpg]]

OCR text:

> 1. How do we get more intelligent Al?
> a. Approach #1 - Larger Parameter Models with Mixture of Experts Models
> b. Approach #2 - Mixture of Agents
> 2. Hands on Workshop
> 3. Q&A with MoE queen + Daniel
> _ * & | a Microsoft ar?
> an if a

![[assets/slides/tzRvcTEapzo/slide-004.jpg]]

OCR text:

> 44
> | : Daria Soboleva
> ; a Head Research Scientist
> ee i MoE Queen
> _ cae E ” Fy oe _ Research focus
> i; ee N +S ve Mixture-of-experts models
> Fe on 4 — : Hardware-efficient LLM training
> ~
> 4 fo Creator of SlimPajama dataset
> head » > p 7 N
> a 7 - 54 (627B tokens, 1IM+ downloads)
> lel] GS a
> =r aa ae Former Google researcher with
> ; " a ott ue expertise in research & engineering
> - # Sy 7 M4 oO
> 4 w | a Microsoft 7
> — ee ee ee

![[assets/slides/tzRvcTEapzo/slide-005.jpg]]

OCR text:

> : r : fe me Qk Qu Geom e Qin 2 Gun . 5 te = whee 8 iw oe
> Balok Te NN 8 Tete DOMES Ura ee pes eeeL o Go . @
> 7 —_—
> 
> |
> 
> Pe ae
> tomes get
> 
> meoes Meo gues tues cate we pane
> sacs .
> uae,
> 
> | mV o
> 
> 4 a Microsoft §=acoct)
> Pd

![[assets/slides/tzRvcTEapzo/slide-006.jpg]]

OCR text:

> won GPU a ee
> Memory is located = ;
> OFFCHIP . - .
> | Tan _ Se oreo eee
> 7 ~ a Microsoft ary?

![[assets/slides/tzRvcTEapzo/slide-007.jpg]]

OCR text:

> 900,0o0 Cores on WSE-3
> AIE 32b Buffers FabricRouter 32b Routes 320
> 32b
> Memory
> 32b
> EachCorehas
> DIRECTACCESS
> TOMEMORY
> Wortd'sFar Microsoft smol?

![[assets/slides/tzRvcTEapzo/slide-008.jpg]]

OCR text:

> Cerebras scales linearly across large models
> Multi-WSE DGX-H100(MuIti-GPU)
> AIE nvlink
> nvlink
> nvlink
> nvlink
> ViaEthernet Activations Activations,Cached Computations Via100sofNVLinks,connectors, switches
> 2024C
> Wortd'sFar aws

![[assets/slides/tzRvcTEapzo/slide-009.jpg]]

OCR text:

> Navigation
> sia Al Configuration Challenge
> ses Cutan)
> Master Prompt Engineering & Multi-Agent Systems
> fe aE) att eo ea tac eRe DT et. is ain
> Wa Deut Biren gtete
> @ Configure Your Al System
> Setup the A agets, mudels, aad gp omges to pecerate the perfect salto!
> Plerwt conte, event cote
> az Drag and drop He here Roureles
> ob monet Conte
> 7 Mae tanperatave ven Weoe ng
> —
> Bes i A rai)
> wi Lick ES ON

![[assets/slides/tzRvcTEapzo/slide-010.jpg]]

OCR text:

> AI Engineer
> World's Fair

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.