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AI data centers have spent heavily on faster accelerators, denser racks and liquid cooling to keep that hardware running. The data pipeline that feeds those processors has not kept up, and expensive compute ends up waiting on memory and storage.
AI factory deployment has long been a race to secure graphics processing units, but the builders of those systems are finding that compute is only half the problem. Data pipelines, storage costs and the clock on getting a cluster running now weigh as heavily as raw horsepower.
Memory has become the scarcest resource in the AI data center, and hyperscalers are rethinking how they hand it out. Composable memory, which pools capacity beyond individual servers and assigns it to workloads on demand, is emerging as one of the most practical answers to that squeeze.
Server consolidation is getting a second look as agentic AI drives a surge in demand for general-purpose compute. Graphics processing units dominate the AI infrastructure conversation, but the agents running on those systems lean heavily on central processing units, and data centers are short on…
Data sovereignty is becoming an architectural problem as enterprises push artificial intelligence inference toward their most sensitive information. Pipelines assembled from separate tools struggle to keep embeddings, permissions and source data aligned, leaving many valuable data sets out of AI’s…
AI factories run on graphics processing units that cost a fortune and work at blistering speed, yet those chips increasingly sit idle waiting for data. As the bottleneck moves from compute to memory and storage, NAND flash is being asked to do far more than hold files.
AI infrastructure networking is approaching a physical turning point as the copper links that tie GPUs together inside a rack strain under ever-faster signaling speeds. The move to light-based interconnects could redraw the boundaries of what counts as a single AI system. Cisco Systems Inc.
Americans have big concerns about artificial intelligence, but that hasn’t stopped OpenAI Inc., Nvidia Corp., IBM Corp. and others from charging ahead. Last week saw shares of Nvidia, CoreWeave Inc., Oracle Corp. and Nebius Group N.V.
Hybrid models interleave state-space model (SSM) layers with a few attention layers. NVIDIA Nemotron 3, Qwen3.5 through Qwen3.8, Kimi-K3, and GLM-5.3-Flash are...
The artificial intelligence infrastructure race is no longer just about building faster chips. AI infrastructure bottlenecks are shifting attention to a bigger challenge: keeping those chips supplied with data, connected through high-speed networks and operating within increasingly demanding power…
AI inference is fast becoming the workload that decides the economics of the AI boom. Training built the first wave of GPU clouds, but serving models faster and cheaper will define the next.
The full-stack AI cloud market is about more than access to graphics processing units: it also encompasses the tools needed to improve models and agents in production. Providers are seeking to distinguish themselves through the tooling, partners and expertise they combine with hardware.
On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are rethinking architectures designed around elastic cloud capacity.
AI model training can leave startups managing infrastructure alongside the products they are trying to build. Willow Care Inc., the company behind AI dictation app Willow Voice, chose CoreWeave Inc. for its forward-deployed support rather than compute alone.
Demand for new data centers, power plants and highways is fueling a construction boom, even as the industry wrestles with declining productivity and a shortage of skilled machine operators. Physical AI, which lets machines perceive their surroundings and act on them, is emerging as one answer.
GPU utilization during post-training depends partly on how efficiently infrastructure moves data and loads updated models. As enterprises continually refine AI agents, reducing delays between training rounds can help keep that process moving.
AI storage strategy is taking on a larger role as enterprises move from successful experiments to systems that must deliver dependable business results. How organizations store, serve and manage data increasingly shapes the performance, cost and practicality of artificial intelligence deployments.
AI teaching agents could make personalized instruction available to more learners by reducing the cost and staffing demands of one-to-one tutoring. MasterClass is exploring that potential through a system designed to adapt to how each student learns.
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