Ai Hardware Buildout

As FM Sitharaman prepares to present the Union Budget, technology companies are urging investments in AI development, digital infrastructure, and market liquidity to accelerate adoption across sectors.

OpenAI signed a multi-year agreement with Cerebras Systems to purchase up to 750 megawatts of computing capacity, in a deal valued at over $10 billion over three years. The contract marks a deliberate move by OpenAI to reduce its dependence on NVIDIA for inference infrastructure at scale. Cerebras builds wafer-scale processors that consolidate compute onto a single silicon die, potentially reducing the inter-chip communication overhead inherent in traditional GPU clusters.

A 1 GW orbital data center would cost roughly $42.4 billion — almost three times its ground-bound equivalent.

General Catalyst is in talks to lead the round for the four-year-old startup, according to our sources.

Neysa is targeting deployments of more than 20,000 GPUs over time as demand for local AI compute accelerates.

C2i has raised $15 million as it tests a grid-to-GPU approach to reducing power losses in AI data centers.

India is ramping up shared AI compute, adding 20,000 GPUs as part of a broader push to attract global AI investment.

When we talk about the cost of AI infrastructure, the focus is usually on Nvidia and GPUs -- but memory is an increasingly important part of the picture.

As the AI hardware space heats up, the iPhone maker has multiple smart products in development.

The partnership will see the two companies exploring how World Labs’ models can work alongside Autodesk’s tools, and vice versa, starting with a focus on entertainment use cases.

OpenAI also plans to expand its presence in India with new offices in Mumbai and Bengaluru later this year.

“I think we're the only quote-unquote manufacturing company out there that has H200 clusters in a data center on site."

Major technology companies collectively pledged approximately $700 billion in AI data center investment for 2026, representing an unprecedented level of coordinated capital expenditure on compute infrastructure. The spending is expected to significantly expand GPU supply and drive down inference costs over a multi-year horizon. In the near term, GPU allocation constraints persist as the primary bottleneck for AI development teams worldwide.

Public backlash over the data center boom is leading to a variety of draconian policies — including bans on new construction.

Many hyperscalers have already made public commitments to cover electricity cost increases.

Here's everything we know about the biggest AI infrastructure projects, including major spending from Meta, Oracle, Microsoft, Google, and OpenAI.

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations

AI data center developers are increasingly relying on a style of camp popularized as housing for men working in remote oil fields.

Sandbar aims to ship the Stream, which can be used to take notes, chat with an AI assistant, and for media playback, this summer.

Meta announced the MTIA 300-500 series, custom AI accelerators designed to reduce dependency on NVIDIA GPUs for its inference and training workloads. The company plans mass deployment by end of 2027. Meta joins Google (TPUs), Amazon (Trainium), and Microsoft (Maia) in pursuing custom silicon to manage compute costs at scale.

The startup, which was created by Rivian founder RJ Scaringe, is looking to train on data from, and deploy in, Rivian's factory.

As the bandwidth and power demands of AI data centers necessitate a transition from electrical to optical scaleup networking, one component has been conspicuously absent from the co-packaged optics arsenal: the laser itself. That’s no longer the case. Last month, Tower Semiconductor and Scintil Phot

Gimlet Labs just raised an $80 million Series A for tech that lets AI run across NVIDIA, AMD, Intel, ARM, Cerebras and d-Matrix chips, simultaneously.

The rise of generative AI has spurred demand for AI workstations that can run or train models on local hardware. Yet modern PCs have proven inadequate for this task. A typical laptop has only enough memory to load a large language model (LLM) with 8 billion to 13 billion parameters—much smaller, and

The company said it would design models, hardware, and interfaces in tandem to deliver a "seamless end-to-end personal intelligence product."

The fundraise includes $1 billion for investing in early-stage startups and $2.5 billion for late-stage growth businesses.

Mistral aims to start operating the data center by the second quarter of 2026.

The startup, which is planning to go public later this year, designs chips specifically for AI inference, another challenger to Nvidia's dominance.

A traditional data center protects the expensive hardware inside it with a “shell” constructed from steel and concrete. Constructing a data center’s shell is inexpensive compared to the cost of the hardware and infrastructure inside it, but it’s not trivial. It takes time for engineers to consider p

CERAWeek — dubbed the Davos of energy — is where policymakers, producers, technologists and financiers gather to discuss how the world powers itself next. NVIDIA and Emerald AI unveiled at the conference last week a new way forward — treating AI factories not as static power loads but as flexible,

The firm says it can reduce the cost of chip development by more than 75% and cut the timeline by more than half.

Meta, Microsoft, and Google are all betting big on new natural gas power plants to run their AI data centers. They may regret it.

Microsoft announced a $10 billion investment in Japan covering AI infrastructure, cybersecurity, and workforce development — the largest single AI infrastructure commitment by a Western company in Asia. The investment targets data sovereignty concerns and positions Azure as the default AI platform for Japanese enterprise customers. Microsoft cited Japan's regulatory environment and demand for local AI capacity as primary drivers.

While browsing our website a few weeks ago, I stumbled upon “How and When the Memory Chip Shortage Will End” by Senior Editor Samuel K. Moore. His analysis focuses on the current DRAM shortage caused by AI hyperscalers’ ravenous appetite for memory, a major constraint on the speed at which large lan

Artificial intelligence harbors an enormous energy appetite. Such constant cravings are evident in the hefty carbon footprint of the data centers behind the AI boom and the steady increase over time of carbon emissions from training frontier AI models.No wonder big tech companies are warming up to n

AWS has an ingrained culture of handling competition, he explained, because the cloud giant also competes with its partners.

The major AI infrastructure was announced in September in partnership was with Nvidia and Nscale.

The two tech giants are looking to co-develop custom chips, at a time when demand for CPUs is high due to a growing global shortage.

Amazon shares have struggled so far this year as investors question the company's aggressive AI spending plans.

Meta's additional spending commitments with CoreWeave will be deployed from 2027 to 2032.

TSMC is benefiting from sustained demand for advanced semiconductors from its key customers like Apple and Nvidia.

The deal comes one day after Meta committed an additional $21 billion in spending to the cloud provider as AI demand continues to soar.

SoftBank is uniting Japan's industrial elite to build the country's own AI foundation, trying to reduce dependence on American and Chinese models. The article Steel giants, automakers, and banks plan to build Japan's answer to US and Chinese AI dominance appeared first on The Decoder.

Surging demand for AI agents is colliding with limited compute capacity. Anthropic is struggling with outages, OpenAI announced the end of Sora, and GPU prices have jumped nearly 50 percent, according to market data. The article The AI industry is running out of compute, with outages, rationing, and

Oracle Corp. agreed to purchase as much as 2.8 gigawatts of fuel-cell power from Bloom Energy Corp. to supply data centers for artificial intelligence work.

Taiwanese stocks rose to a new record as investors returned to the pre-Iran war trading theme of chasing AI shares amid hopes for easing tensions in the Middle East.

Taiwanese stocks rose to a new record as investors returned to the pre-Iran war trading theme of chasing AI shares amid hopes for easing tensions in the Middle East.