Project Flux
Project Flux is a pioneering podcast that explores the transformative impact of Artificial Intelligence across various industries, including construction, music, infrastructure, and life sciences, with a focus on revolutionising the project delivery profession. Each episode brings to light how AI is redefining efficiency, innovation, and strategic decision-making in project management. Through engaging conversations with industry leaders, technologists, and forward-thinkers, the podcast offers a comprehensive look at the challenges and opportunities AI introduces, the nuances of its integration, and its potential to reshape project delivery on a global scale.
Project Flux is the go-to resource for professionals seeking to navigate the evolving landscape of AI in project delivery, offering insights, strategies, and inspiration to harness the power of AI for a more innovative and effective future.
Project Flux
Why AI Models Are Choking — And How to Fix It
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
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