Nvidia’s acquisition of Arm strengthens its ecosystem, brings economies of scale to the cloud, expansion to the edge
Nvidia is after a double bottom line in the AI chip market: Better performance and better economics. Arm’s acquisition helps with the economies of scale in the data center and expands Nvidia’s footprint to the edge
Arm’s acquisition by Nvidia has been rumored for a while, and now, it has been officially confirmed. This is a significant and well-tuned move for both sides. One that has been long-time coming, in fact. We review the steps leading to this outcome, and what this means for the AI chip market.
This is the second high-profile acquisition for Nvidia in 2020, following the acquisition of Mellanox in April. The two are complementary, as they are both fundamental for Nvidia’s plan to acquire and maintain a leading role in AI workloads in the data center and beyond.
As we have noted, GPUs are a boon for machine learning workloads. Nvidia has also taken note and acted upon this early and successfully. This has effectively resulted in an additional market and a significantly growing one for that matter. Machine learning is eating the world, alongside the cloud.
Machine learning workloads are a good match for the cloud. For starters, the training phase for machine learning algorithms is quite demanding in terms of compute. For many organizations, it does not make sense to purchase the kind of infrastructure required for those workloads, and this is where the cloud comes into play.