#ai-infrastructure

#ai-infrastructure

Nvidia (NASDAQ: NVDA) has firmly established itself as the leading player in artificial intelligence (AI) infrastructure, as it accounts for nearly 92% of the data center GPU market. That dominance has been the foundation of its robust financial performances of recent years. In its fiscal 2026 second quarter (which ended July 27), Nvidia reported revenues of $46.7 billion, up 56% year over year and exceeding guidance, while its GAAP ( generally accepted accounting principles) gross margin was 72.4%.

Mortgage rate squeeze: AI investments are diverting capital from the housing market, contributing to the housing affordability crisis and impacting home sales. Utilities impact: The AI industry's high energy demands are driving utilities to expand capacity, with projects like nuclear reactors aiming to meet data center needs, potentially benefiting consumers with stable pricing. Water consumption: AI data centers are significant water consumers, leading to shortages and increased utility bills for residents, exacerbating housing market pressures.

Dell Technologies reported record revenue of $29.8 billion in the second quarter of fiscal 2026, up 19 percent from a year earlier. Earnings per share rose 38 percent to $1.70, while adjusted earnings were $2.32. Revenue from servers and networks grew 69 percent to $12.9 billion, driven by strong demand for AI systems. COO Jeff Clarke said Dell shipped more AI solutions in the first half of the year than it did in all of 2025

"It's not just computing capacity that contributes to the cost of AI: IT needs to reexamine existing storage operations too, Kimball said. "I would take a long look at my storage infrastructure and how to better optimize on and off prem. The infrastructure populating most enterprise datacenters is out of date and underutilized. Moving to servers that have the latest, densely populated CPUs is a first start," he said.

Mako, a startup co-founded by Cornell Tech assistant professor Mohamed Abdelfattah, has raised $8.5 million in seed funding to tackle one of artificial intelligence's most pressing infrastructure challenges: optimizing the computing efficiency of graphics processing units, or GPUs. The $8.5 million round was led by M13, with participation from Neo, Flybridge, and others. The company plans to use the funding to grow its engineering team and accelerate product development.

In Q4, revenue, gross margin and operating margin were at the high end of our guidance ranges, earnings per share was above the guidance range and we delivered solid operating cash flow.

Join Coursedog's Platform team to architect and deliver the AI infrastructure powering Intelligent Academic Operations. You'll design orchestration pipelines and AI integration frameworks.

LM Caches play a critical role in improving the efficiency and scalability of deploying large language models by caching and reusing previously computed results.

Q2 revenue is expected to grow +144.3% YoY, reflecting strong demand in automotive and smart devices, but profitability remains distant due to high reinvestment in AI.

Accessing critical AI infrastructure is hindered by fundamental problems: crushing costs, extreme concentration, and insufficient capacity. A company seeking large-scale compute must often sign long-term contracts with hyperscalers, far beyond the budgetary reach of most academics and startups.

Google will invest $25 billion over the next two years to expand data centers and AI infrastructure across the PJM Interconnection to meet surging AI workload demand.

Amazon Bedrock AgentCore enables enterprise-grade services for deploying AI agents at scale, providing infrastructure, tools, and controls for trustworthy operations.

"Customers want a model where on-premises infrastructure offers the same flexibility as the cloud. So scalable, quick to deploy, and without long delivery times," Bal explains.

Beverly Morris fears drinking water from her well due to sediment buildup she attributes to the nearby AI data center. "I'm afraid to drink the water, but I still cook with it, and brush my teeth with it," she expressed.

LangChain has achieved impressive growth since its inception, transforming from an open-source project to a startup, securing major funding and amassing significant popular usage.

Developing and integrating interconnects is no small order. It's arguably the reason Nvidia is the powerhouse it is today.