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Next ’25 Infrastructure Innovations: Building the Foundation for AI at Scale

Image by Luke Jones

At Google Cloud Next ’25, several key infrastructure advancements were unveiled, designed to help businesses run AI and enterprise workloads with top-tier performance, global connectivity, and flexibility. Here’s a breakdown of the key updates.

Cloud WAN: Lightning-Fast Networking

Google Cloud’s new Cloud WAN is now available, offering:
 

  • Ultra-fast connections with near-zero latency for billions of users worldwide.

  • Up to 40% better network performance and 40% lower costs compared to traditional solutions.
     

This global network backbone connects data centers, branches, and campuses at "Google speed," ensuring fast and reliable connections.

AI Hypercomputer & Ironwood TPU: Powering AI at Scale

The AI Hypercomputer platform has received powerful updates to help businesses get the most out of their AI workloads:
 

  • Ironwood TPU (7th generation) pods offer 42.5 exaFLOPS, up to 4 times the performance of previous models.

  • vLLM for TPUs improves large-language model performance across platforms like Compute Engine and Vertex AI.

  • Pathways for Google Cloud provides a unified machine learning runtime for large-scale training and inference.
     

These upgrades mean faster model training, fewer bottlenecks, and better ROI on AI investments.

Workload-Optimized Storage & Compute: Supercharging Performance

New tools to support data-heavy and compute-intensive applications:
 

  • Rapid Storage (Preview): Zonal storage with <1 ms latency and 20x faster access than standard regional buckets.

  • Anywhere Cache (GA): A cache that reduces latency by up to 70%, keeping data close to GPUs/TPUs for faster processing.

  • Hyperdisk Exapools: Exabyte-scale block storage delivering TiB/s throughput for high-performance computing (HPC) and AI workloads.
     

These updates ensure data is processed faster and more efficiently, minimizing delays and maximizing throughput.

Google Distributed Cloud: AI On-Premises with Gemini

For businesses with specific data residency or low-latency needs, Gemini on Google Distributed Cloud (still in pre-announcement) offers:
 

  • On-premises AI running on NVIDIA DGX/HGX platforms with Gemini models.

  • Agentspace Search for secure, enterprise-level search and automation without compromising data residency.
     

This allows businesses to run powerful AI tools on-premises, ensuring compliance and smooth performance.

What It Means for Businesses

These innovations create the strong infrastructure foundation needed for next-generation AI and enterprise applications. With high-performance networking, powerful compute options, ultra-fast storage, and flexible on-premises solutions, businesses can scale their operations with confidence – whether it's for training complex models, running real-time AI services, or connecting global teams.

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