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Subvolts take AI needs specialized clouds to feed its massive GPU demands. Jan 6, 2026
For you
Developers
Learn about specialized infrastructure needs for optimizing large AI model training and GPU utilization.
Business
Understand the emerging "Neo Cloud" model and how specialized providers address unique AI compute demands.
Hype note
3/5Notable

This interview provides insight into a significant infrastructure provider enabling large-scale AI development beyond major cloud platforms.

Gradient Dissent
Gradient Dissent · S1 · Ep 134 Episode

Inside the $41B AI Cloud Challenging Big Tech | CoreWeave SVP

53 min Interview Hosts: Lukas Biewald
0:0053:19

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From Subvolts

Summary and analysis by Subvolts editorial, AI-assisted.

Summary

Lukas Biewald hosts CoreWeave SVP Corey Sanders on Gradient Dissent to discuss the evolving needs of AI training. Sanders explains why general-purpose clouds struggle with large-scale AI workloads, introducing the "Neo Cloud" concept. The conversation covers specialized hardware and software stacks designed to maximize GPU utilization and achieve high "goodput" for modern AI models, emphasizing AI's demand for specialization.

Explained simply

Training big AI models needs lots of computer power, like feeding a giant machine. Regular computer clouds struggle with this. This episode explains a new type of "Neo Cloud" built just for AI, making sure the machines get enough power to work fast.

Why listen

This episode explains the infrastructure challenges behind massive AI model training. It's for anyone building or deploying large AI systems, showing how specialized cloud providers like CoreWeave emerge to meet demands that general clouds cannot. This directly relates to frontier labs like Google Gemini and OpenAI, who constantly push the boundaries of model size and complexity, requiring immense, specialized compute power. Understanding this hardware bottleneck is crucial for anyone following AI's practical development.

On the mic
  • HostLukas Biewald
  • GuestCorey SandersSVP, CoreWeave
About
Hardware
Key facts
  • Guest: Corey Sanders, CoreWeave SVP
  • Show: Gradient Dissent
  • Length: 53 minutes
  • Core concept: AI demands specialization, leading to a "Neo Cloud" model.
  • Problem addressed: General-purpose clouds break down under large-scale AI workloads.
Show notes from Lukas Biewald

The future of AI training is shaped by one constraint: keeping GPUs fed.

In this episode, Lukas Biewald talks with CoreWeave SVP Corey Sanders about why general-purpose clouds start to break down under large-scale AI workloads.

According to Corey, the industry is shifting toward a "Neo Cloud" model to handle the unique demands of modern models.

They dive into the hardware and software stack required to maximize GPU utilization and achieve high goodput.

Corey’s conclusion is clear: AI demands specialization.

Connect with us here:

Corey Sanders: https://www.linkedin.com/in/corey-sanders-842b72/

CoreWeave: https://www.linkedin.com/company/coreweave/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

(00:00) Trailer

(00:57) Introduction

(02:51) The Evolution of AI Workloads

(06:22) Core Weave's Technological Innovations

(13:58) Customer Engagement and Future Prospects

(28:49) Comparing Cloud Approaches

(33:50) Balancing Executive Roles and Hands-On Projects

(46:44) Product Development and Customer Feedback

Jargon decoded
goodput
Corey Sanders
the actual amount of useful work completed, not just the speed of data transfer.
Published
Show
Gradient Dissent · Lukas Biewald
Length
53 min
Summary by
Subvolts, using an AI model (how we work). Spotted a mistake? Tell us.
#Hardware

Best of Gradient Dissent

Same guests, other shows

Questions people ask

Why are general-purpose clouds insufficient for AI training?
General-purpose clouds break down under the unique and large-scale demands of modern AI training workloads, particularly in keeping GPUs consistently utilized.
What is the "Neo Cloud" model?
The "Neo Cloud" is a specialized cloud infrastructure model designed to handle the unique demands of modern AI models, focusing on maximizing GPU utilization and achieving high goodput.

Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Lukas Biewald.