Originally posted on Evocative.

Explore how scalable, proximity-optimized infrastructure – like high-density colocation – is becoming critical for the future of AI

Artificial Intelligence (AI) is transforming how we live, work, and interact — powering everything from personalized mobile apps to predictive business operations. Analysts project that AI could contribute up to 15% of global economic output over the next decade.

But as organizations scale up AI investments, they face a critical infrastructure question: how do you place the right compute, at the right density, in the right location — fast enough to keep up? With early AI adoption already pushing the limits of computing, storage, and processing, we’ll explore why scalable, proximity-optimized infrastructure — like high-density colocation — is becoming critical for the future of AI.

The Infrastructure Challenge Behind AI Adoption

AI workloads — especially those involving deep learning and large language models (LLMs) — demand enormous computational power and specialized hardware like GPUs and TPUs. These workloads are data-intensive, power-hungry, and generate significant heat, requiring high-performance environments to operate efficiently.
AI deployments typically involve two major phases, each with unique infrastructure challenges:
  • AI Training is the phase where models are taught to recognize patterns and relationships in massive datasets. It’s compute- and power-intensive, requiring high-density environments with robust cooling, often including liquid systems.  
  • AI Inference happens after training, when models are put into production to deliver real-time outputs or predictions. It may run on lighter infrastructure, but it demands proximity and low-latency response — especially for edge-based or interactive applications. 

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