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Amazon reportedly closes $7B Decart deal, eyes AI hardware optimization

·StartupReader editorial desk· 2 min readReviewed by our editors
ACQUISITION. Decart acquired by Amazon. $7B Deal value. Decart Optimization Stack (DOS) from Decart, AI model-hardware sync.

Amazon is set to acquire Decart, the AI startup behind the Decart Optimization Stack (DOS), for roughly $7 billion, according to earlier reports. If completed, the deal would mark one of the largest AI acquisitions of 2026, reflecting growing interest in software that bridges gaps between AI models and specialized processors.

DOS, as described in third-party analysis, aims to simplify the process of syncing AI models with different hardware types, potentially reducing reliance on custom programming libraries.

The reported acquisition arrives amid a wave of high-profile AI deals this year, including Nvidia’s purchase of Hugging Face and Google’s acquisition of Mechanize. Unlike those targets, Decart’s technology appears centered on the practical challenges of running AI workloads at scale, rather than model development or open-source adoption.

For the acquiring company, the deal may signal a push to strengthen its position in the AI infrastructure space, where competitors have also made significant investments. While the buyer’s existing cloud platform already offers AI services, DOS could offer a way to speed up deployment for customers managing complex models across diverse environments. The valuation—significantly higher than some recent AI deals—hints at the perceived value of tools that reduce hardware-related friction.

Details about integration plans remain scarce. Decart’s public profile describes its work on real-time world models, which could either complement existing cloud offerings or evolve into a distinct product line. Industry watchers may look for clues about whether Decart’s team will take on leadership roles, as seen in other recent acquisitions.

Sources: seekingalpha.com

“The move suggests Amazon may be betting on tools to ease AI workload deployment across varied hardware, a challenge Decart’s stack appears designed to solve.”
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