---
title: "Slides: Under 5 minutes to a deployed LLM endpoint — Audry Hsu, RunPod"
category: "slides"
video_id: "ILdE7FaAjVA"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Under 5 minutes to a deployed LLM endpoint — Audry Hsu, RunPod

## Source Video
[Under 5 minutes to a deployed LLM endpoint — Audry Hsu, RunPod](https://www.youtube.com/watch?v=ILdE7FaAjVA)

## 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/ILdE7FaAjVA/slide-001.jpg]]

OCR text:

> runpod
> AIE
> Introducing
> Runpod
> The foundationalplatformforbuilding.
> running.and scaling custom Al systems.
> GoogleDeepMind

![[assets/slides/ILdE7FaAjVA/slide-002.jpg]]

OCR text:

> i. St eee 90 Oo
> Why Runpod Exists
> Pe 3
> bd *
> , ,
> oe G G G
> a ad
> Infrastructure eats GPU access is Builder primary focus
> developer time slow and opaque should be building
> — 4
> |
> = ., | AlEngineer |
> . 7 a EUROPE
> oo oe

![[assets/slides/ILdE7FaAjVA/slide-003.jpg]]

OCR text:

> . oe Capea 20 @08
> con re re sa
> Serverless
> roe Autoscaling inference without
> a Py infrastructure overhead
> ae
> va * Best for
> sail e@ Real-time inference
> @ Vanable or spsky traffic
> e User-facing Al products
> Why teams use it
> * Nopre-provsionmng
> « Scales automatically
> ® Pay only for usage
> a eS ) |
> | | Al Engineer |
> 7 ae, le EUROPE
> ne ite:
> ae, EB u

![[assets/slides/ILdE7FaAjVA/slide-004.jpg]]

OCR text:

> &) runpod
> ss  h0UmUmrtw~”~C~—
> = _ J Engineering the future of Al

![[assets/slides/ILdE7FaAjVA/slide-005.jpg]]

OCR text:

> Q
> v2.4.0
> VLLM
> AIE
> 2U7
> Finet
> VLLM
> VLLM
> Google DeepMind

![[assets/slides/ILdE7FaAjVA/slide-006.jpg]]

OCR text:

> Par . ae
> ‘ " ore
> a * Le re
> a in wd err
> wa * = arn
> a 7
> a rel. | AlEngineer |
> ao a Va

![[assets/slides/ILdE7FaAjVA/slide-007.jpg]]

OCR text:

> | Al Engineer |
> aU elas
> AI.ENGINEER


## 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.