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Why AI Models Are Choking — And How to Fix It

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Manage episode 477673161 series 3567994
内容由Project Flux提供。所有播客内容(包括剧集、图形和播客描述)均由 Project Flux 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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In this episode of the Project Flux podcast, we speak with JB Baker, the VP of Marketing and Product Management at ScaleFlux, about the critical role of storage in AI infrastructure. They discuss the importance of efficient data pipelines, the challenges posed by the memory wall, and the revolutionary concept of computational storage. JB emphasises the need for businesses to consider the full impact of AI deployment on their infrastructure, including storage and memory technologies. The conversation also touches on sustainability in AI, the future of quantum computing, and the potential of decentralised AI systems.
Takeaways

  • Efficient data pipelines are crucial for maximizing GPU performance.
  • The memory wall is a significant barrier to AI scalability.
  • Computational storage can enhance data management efficiency.
  • Businesses often overlook the importance of storage when investing in AI.
  • AI workloads generate more data than traditional workloads.
  • Query engineering is essential for effective AI model usage.
  • Sustainability is a growing concern in AI infrastructure.
  • The future of AI will involve specialized processors for specific tasks.
  • Quantum computing presents both opportunities and challenges for AI.
  • Decentralized AI systems may change the landscape of data processing.

Chapters
00:00 Introduction to Storage Challenges in AI
04:14 The Data Pipeline: Bridging Storage and Processing
07:20 Understanding Computational Storage
10:14 Challenges in Implementing Advanced Storage Solutions
13:18 Common Mistakes in AI Infrastructure Upgrades
16:17 The Energy Demands of AI and Storage
19:05 Navigating AI Querying and User Experience
22:09 Balancing Innovation and Sustainability in AI Infrastructure
26:55 The Future of Energy Consumption
28:43 Innovations in Data Center Cooling
30:26 Quantum Computing and Its Implications
33:43 Exploring Alternative Computing Methods
35:12 The Evolution of AI Infrastructure
40:22 Decentralized AI and Edge Computing
42:31 Emerging Technologies in Memory and Storage
49:12 Introduction to Project Flux and Partnership
49:42 Future of Project Management Insights
49:57 Introduction and Sponsorship Highlights
50:20 Engagement and Community Building
50:33 Unleash the Code Intro.wav
JB's Linked In

Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify

Support the show

Subscribe to the Project Flux Newsletter!

All our podcasts commentate on the weeks hottest news brought to you by the Project Flux Newsletter!

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Project Data Analytics Taskforce site

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章节

1. Why AI Models Are Choking — And How to Fix It (00:00:00)

2. [Ad] Everyday AI: Your daily guide to grown with Generative AI (00:11:12)

3. (Cont.) Why AI Models Are Choking — And How to Fix It (00:11:59)

122集单集

Artwork
icon分享
 
Manage episode 477673161 series 3567994
内容由Project Flux提供。所有播客内容(包括剧集、图形和播客描述)均由 Project Flux 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Send us a text

In this episode of the Project Flux podcast, we speak with JB Baker, the VP of Marketing and Product Management at ScaleFlux, about the critical role of storage in AI infrastructure. They discuss the importance of efficient data pipelines, the challenges posed by the memory wall, and the revolutionary concept of computational storage. JB emphasises the need for businesses to consider the full impact of AI deployment on their infrastructure, including storage and memory technologies. The conversation also touches on sustainability in AI, the future of quantum computing, and the potential of decentralised AI systems.
Takeaways

  • Efficient data pipelines are crucial for maximizing GPU performance.
  • The memory wall is a significant barrier to AI scalability.
  • Computational storage can enhance data management efficiency.
  • Businesses often overlook the importance of storage when investing in AI.
  • AI workloads generate more data than traditional workloads.
  • Query engineering is essential for effective AI model usage.
  • Sustainability is a growing concern in AI infrastructure.
  • The future of AI will involve specialized processors for specific tasks.
  • Quantum computing presents both opportunities and challenges for AI.
  • Decentralized AI systems may change the landscape of data processing.

Chapters
00:00 Introduction to Storage Challenges in AI
04:14 The Data Pipeline: Bridging Storage and Processing
07:20 Understanding Computational Storage
10:14 Challenges in Implementing Advanced Storage Solutions
13:18 Common Mistakes in AI Infrastructure Upgrades
16:17 The Energy Demands of AI and Storage
19:05 Navigating AI Querying and User Experience
22:09 Balancing Innovation and Sustainability in AI Infrastructure
26:55 The Future of Energy Consumption
28:43 Innovations in Data Center Cooling
30:26 Quantum Computing and Its Implications
33:43 Exploring Alternative Computing Methods
35:12 The Evolution of AI Infrastructure
40:22 Decentralized AI and Edge Computing
42:31 Emerging Technologies in Memory and Storage
49:12 Introduction to Project Flux and Partnership
49:42 Future of Project Management Insights
49:57 Introduction and Sponsorship Highlights
50:20 Engagement and Community Building
50:33 Unleash the Code Intro.wav
JB's Linked In

Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify

Support the show

Subscribe to the Project Flux Newsletter!

All our podcasts commentate on the weeks hottest news brought to you by the Project Flux Newsletter!

Get a step ahead on the world of AI and tech, subscribe and get the weekly newsletter to your inbox every Monday!

Sign Up to Newsletter
Project Data Analytics Taskforce site

  continue reading

章节

1. Why AI Models Are Choking — And How to Fix It (00:00:00)

2. [Ad] Everyday AI: Your daily guide to grown with Generative AI (00:11:12)

3. (Cont.) Why AI Models Are Choking — And How to Fix It (00:11:59)

122集单集

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