
The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes. Silicon Data […]
Compute has become the largest cost for companies building AI products, with hundreds of billions of dollars flowing annually into data centers and GPUs. However, there is currently no straightforward way to price compute or for firms to protect themselves against price changes. A startup has closed funding to create a reference price for GPU rental and an index that would underlie a Wall Street futures contract, with plans to launch compute futures trading pending regulatory approval.
NVIDIA announced a new 64GB configuration of DGX Spark — from Acer, ASUS, Dell, Gigabyte, HP and MSI — its GB10-powered desktop AI system. It gives developers a way to start with one system for local models and agents, then cluster two 64GB units for 128GB of memory across the cluster and more compute when workloads grow. The direct message is: Run open models and always-on agents on your own desk, then cluster DGX Spark system as the work grows – instead of on a metered API. The ti

On Friday, Amazon committed to donating more than $1 billion over the next five years to communities neighboring data centers. In a press release, Amazon Web Services’ CEO Matt Garman said that communities would decide how to prioritize funds across “education, job training, energy affordability, water, and energy preservation, and local priorities.” Laying out other community commitments, Amazon also promised to ensure data centers wouldn’t increase power bills or wipe out local water supplies.

AI success hinges on infrastructure. Use this five-part framework to evaluate power, cooling, security, cost strategy, and scalability.
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