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VIAI has outgrown traditional cloud infrastructure. A new category is emerging: NeoClouds.
๐ง๐ฟ๐ฎ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐๐น๐ผ๐๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด data centers were designed for general workloads: Web apps, Databases, Storage, Virtual machines.
AI workloads are different.
Training frontier models requires tightly coupled GPUs, extreme networking bandwidth, and sustained high-density power delivery.
NVIDIA CEO Jensen Huang calls these environments ๐๐ ๐๐ฎ๐ฐ๐๐ผ๐ฟ๐ถ๐ฒ๐.Facilities where compute and data go in, and trained intelligence comes out.
NeoClouds are the operators building them like
CoreWeave, Lambda, Nebius.
They were purpose-built for GPU-intensive AI training from day 1, not retrofitted from general cloud architecture.
๐ช๐ต๐ฎ๐ ๐ ๐ฎ๐ธ๐ฒ๐ ๐ก๐ฒ๐ผ๐๐น๐ผ๐๐ฑ๐ ๐๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐
โ๏ธ Massive GPU clusters functioning as a single supercomputer
โ๏ธ Bare-metal performance with minimal abstraction layers
โ๏ธ Ultra-low latency networking for distributed training
โ๏ธ Advanced liquid cooling to sustain high thermal loads
For large-scale model training, this changes economics and timelines dramatically.
๐ค Hyperscalers are not disappearing. They are partnering. Building AI-scale infrastructure takes years, and model demand is compounding now.
We are seeing the specialization of cloud infrastructure in real time.
Understanding where your workload fits is no longer optional. It affects cost, performance, and competitive advantage.
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๐ Hi, Iโm Viktoria, Principal AI Evangelist at Databricks. I make complex AI accessible. Follow for more!
#AIExplained #NeoCloud #AIFactory #CloudComputing #LearnAI
@viktoria.semaan
