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Inside Google’s Ironwood: AI Inference, Performance & Data Protection
In this episode of The Deep Dive, we unpack Google’s 7th-gen TPU, Ironwood, and what it means for the future of AI infrastructure. Announced at Google Cloud Next, Ironwood is built specifically for AI inference at scale, boasting 4,614 TFLOPs, 192 GB of RAM, and breakthrough bandwidth.
We explore:
- Why inference optimization matters more than ever
- How Ironwood compares to Nvidia, AWS, and Microsoft’s chips
- The rise of sparse core computing for real-world applications
- Power efficiency, liquid cooling, and scalable AI clusters
- What this means for data protection, governance, and infrastructure planning
This episode is essential for IT leaders, cloud architects, and AI practitioners navigating the explosion of AI workloads and the growing complexity of data management.
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By TechDaily.aiIn this episode of The Deep Dive, we unpack Google’s 7th-gen TPU, Ironwood, and what it means for the future of AI infrastructure. Announced at Google Cloud Next, Ironwood is built specifically for AI inference at scale, boasting 4,614 TFLOPs, 192 GB of RAM, and breakthrough bandwidth.
We explore:
- Why inference optimization matters more than ever
- How Ironwood compares to Nvidia, AWS, and Microsoft’s chips
- The rise of sparse core computing for real-world applications
- Power efficiency, liquid cooling, and scalable AI clusters
- What this means for data protection, governance, and infrastructure planning
This episode is essential for IT leaders, cloud architects, and AI practitioners navigating the explosion of AI workloads and the growing complexity of data management.