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GPU Cloud Deployment Without Leaving Your IDE — Audry Hsu, RunPod

index_state ready data_status ok

AI Engineer· published 2026-06-09· 0:20:18· en-US· indexed 2026-08-11 10:56

Open on YouTube

Scene timeline

  1. Shot 0, 0:00 to 0:05, 1 of 1 keyframes kept
  2. Shot 1, 0:05 to 0:09, 1 of 1 keyframes kept
  3. Shot 2, 0:09 to 0:14, 1 of 1 keyframes kept
  4. Shot 3, 0:14 to 0:38, 1 of 1 keyframes kept
  5. Shot 4, 0:38 to 1:02, 1 of 1 keyframes kept
  6. Shot 5, 1:02 to 1:35, 1 of 1 keyframes kept
  7. Shot 6, 1:35 to 2:16, 1 of 1 keyframes kept
  8. Shot 7, 2:16 to 3:05, 1 of 1 keyframes kept
  9. Shot 8, 3:05 to 3:52, 1 of 1 keyframes kept
  10. Shot 9, 3:52 to 4:20, 1 of 1 keyframes kept
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  13. Shot 12, 5:17 to 5:24, 1 of 1 keyframes kept
  14. Shot 13, 5:24 to 5:33, 1 of 1 keyframes kept
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  16. Shot 15, 5:34 to 5:36, 1 of 1 keyframes kept
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  18. Shot 17, 5:37 to 5:40, 1 of 1 keyframes kept
  19. Shot 18, 5:40 to 5:42, 1 of 1 keyframes kept
  20. Shot 19, 5:42 to 6:11, 1 of 1 keyframes kept
  21. Shot 20, 6:11 to 6:41, 1 of 1 keyframes kept
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  34. Shot 33, 11:59 to 12:36, 1 of 1 keyframes kept
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  40. Shot 39, 14:05 to 14:27, 0 of 1 keyframes kept
  41. Shot 40, 14:27 to 14:41, 1 of 1 keyframes kept
  42. Shot 41, 14:41 to 15:15, 1 of 1 keyframes kept
  43. Shot 42, 15:15 to 15:48, 1 of 1 keyframes kept
  44. Shot 43, 15:48 to 15:50, 1 of 1 keyframes kept
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  48. Shot 47, 16:49 to 16:52, 0 of 1 keyframes kept
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  53. Shot 52, 17:15 to 17:16, 0 of 1 keyframes kept
  54. Shot 53, 17:16 to 17:18, 1 of 1 keyframes kept
  55. Shot 54, 17:18 to 17:19, 1 of 1 keyframes kept
  56. Shot 55, 17:19 to 17:47, 1 of 1 keyframes kept
  57. Shot 56, 17:47 to 17:56, 1 of 1 keyframes kept
  58. Shot 57, 17:56 to 18:35, 1 of 1 keyframes kept
  59. Shot 58, 18:35 to 19:12, 1 of 1 keyframes kept
  60. Shot 59, 19:12 to 19:50, 1 of 1 keyframes kept
  61. Shot 60, 19:50 to 20:03, 1 of 1 keyframes kept
  62. Shot 61, 20:03 to 20:18, 1 of 1 keyframes kept

62 shot(s).

keyframes kept every frame deduplicated

What was stored

cues
225
whisperx 225
chunks
35
from 225 cues
keyframes
42
kept of 62 captured
frames with text
42
2,208 lines read
chapters
0
from the source metadata
keyframe bytes
7.1 MB
word timings on 225 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage state model started took
fetch done — 2026-08-11 10:52 1m 17s
stt done — 2026-08-11 10:53 18s
chunk done — 2026-08-11 10:54 0s
text_embed done — 2026-08-11 10:54 0s
keyframe done — 2026-08-11 10:54 1m 47s
ocr done — 2026-08-11 10:56 32s
frame_embed done — 2026-08-11 10:56 7s

Frames, and what the machine read

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  • 4:24 #10 skipped

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  • 5:14 #11 skipped

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  • 5:19 #12 done20 line(s)

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  • 5:38 #17 done56 line(s)

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  • 5:42 #18 done93 line(s)

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  • 5:48 #19 done84 line(s)

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    26. ★1.00
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    33. from runpod_flash inpoct Endpoint, SouType0.96
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    35. ★1.00
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    41. # Mark the function below for remote execution0.98
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    48. print(f"metta from your GPut ((gpu_name})")0.89
    49. return {"gpu": gpu_nane}0.91
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    62. Get started1.00
    63. Create handler functions0.99
    64. Runpod console0.97
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    68. 40.65
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Transcript

225 cues· 2,423 words· 12,781 chars

  1. 0:14 Hey, everyone.
  2. 0:16 I'm Audrey.
  3. 0:17 I work at Rumpod.
  4. 0:21 Were any of you in my earlier session?
  5. 0:22 OK, good.
  6. 0:26 This intro is the same, but what I'm going to show is a little bit different.
  7. 0:31 Has anyone heard of Rumpod or used Rumpod before?
  8. 0:35 You have.
  9. 0:35 Do you mind if I ask you how you've used us or heard about us before?
  10. 0:45 Yeah, for LLM training.
  11. 0:45 LLM training?
  12. 0:46 OK, at your university?
  13. 0:46 And where do you go to uni?
  14. 0:48 Oxford.
  15. 0:49 Oxford?
  16. 0:49 I did a study abroad there one summer.
  17. 0:51 It's awesome there.
  18. 0:52 OK, love it.
  19. 0:54 OK, and your name is?
  20. 0:56 Yunus.
  21. 0:56 Yunus.
  22. 0:56 OK, so Yunus might know a little bit about this already, but I'll talk to you guys through a little intro.
  23. 1:02 What do we do?
  24. 1:04 We're a AI cloud infrastructure company.
  25. 1:08 And our mission is to build the foundational platform for
  26. 1:13 developers to scale their AI workloads.
  27. 1:16 And basically what that means is we bring the hardware, we bring the GPUs and the compute.
  28. 1:23 We make it easy for you guys to bring your code, bring your models and deploy as quickly as possible.
  29. 1:28 We don't want you spending time configuring infrastructure and thinking about things like scaling.
  30. 1:39 Why RemPod exists, a lot of teams that we've talked to, they are all wrestling with the same thing, that infrastructure.
  31. 1:45 They're spending more time with the infrastructure than they are with the models.
  32. 1:48 So things like CUDA version alignment, like what versions of PyTorch run well together, which
  33. 1:59 which new of the GPU skews have been tested and figuring out the bugs there.
  34. 2:04 So a lot of that are things that we try to take that configuration problem away from you guys.
  35. 2:11 So you guys could just focus on training your model or building your apps.
  36. 2:17 And a little bit of a backstory about our company.
  37. 2:20 So this is Zen and Pradeep, our two founders.
  38. 2:23 They started Rumpod in 2022.
  39. 2:26 They had a failed crypto mining venture.
  40. 2:30 So they had a bunch of spare GPUs in their basement.
  41. 2:34 They built a prototype of what is the foundation of Rumpod today.
  42. 2:39 And they just posted on Reddit and said, does anyone want some free GPUs in exchange for feedback?
  43. 2:47 And that is literally how our company started.
  44. 2:49 And ever since then, we've been building in public with the community.
  45. 2:54 So we've been revenue generating from the very beginning, which is very, very rare.
  46. 3:00 And even today, we have around 500 developers on our platform.
  47. 3:06 We're in 30-plus data centers across 10 countries.
  48. 3:10 In Europe, that includes France, Romania, Iceland, if that's part of Europe, Asia Pacific.
  49. 3:20 And we recently hit a pretty big milestone of $120 million in annual recurring revenue.
  50. 3:27 This is just a quick glance of some of the customers that we have.

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