Nvidia has officially announced to acquire Deci- an Israeli AI company with an estimated value of around $300 million. It has turned out to be a Chip Gaint with this acquisition.
Just one week before it acquired Run:ai- an AI infrastructure orchestration and management platform with a $700 million value. In broader terms, Nvidia has spent $1 Billion to Purchase Two Startups in a Week
Nvidia’s expertise will complemented by Israeli’s AI Deci which will improve performance with the design of deep learning models. Deci’s company team has knowledge about Foundational Models in NAS which will be beneficial for Nvidia.
Along with the acquisition of Run:ai, it is just like ‘ Icing on the cake’ because it is its biggest purchase in Israel since its last purchase of $6.9 billion of Mellanox in March 2019.
With this its R&D center in Israel has grown by 100%, reaching 4,000 employees in the country. In 2019 Nvidia also announced its acquisition of the Tel-Aviv-based startup that provides high-performance software-defined storage.
In 2016 it established its first R&D center in Israel which is now headed by Amit Krig, SVP software and NIC Product line at Nvidia.
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Now let’s see the details of both of the acquisitions in sequence.
Features Of Deci- An Israeli AI Company
It is an end-to-end learning platform Founded in 2019 by Yonatan Geifman, Jonathan Elial Ran El-Yaniv, and Ran El-Yaniv. This platform helps AI developers to optimize, build, and locate AI models in any place such as mobile, edge, or cloud.
The Deci platform has helped scientists to remove the lack of AI by grasping a more efficient developed model.
This platform is allowing AI developers to take benefit of (NAS) Neural Architecture Search which will help them to develop highly refined deep learning models in very little time to meet their special goals.
Deci has one more striking feature of Generative AI that is designed specially to meet the privacy needs and efficiency of enterprises. this platform has several features of a series of proprietary, an inference engine, an AI inference cluster management solution, and large language models (LLMs).
Features of Run:ai – An AI Infrastructure Management Platform
It is an AI infrastructure management and orchestration platform. It was founded in 2018 by Dr.Ronen Dar (CTO) and Omri Geller (CEO) .
Dr. Ronen Dar was working in Anobit Technologies as an algorithm engineer and was a researcher at Bell Labs. before acquiring the company he also worked at Apple.
Besides this Omri Geller (CEO) has worked at the Prime Minister’s Office Technological Unit.
Run:ai has raised $118 million in funding in two rounds starting from $75 million in March 2022 led by Insight partners and Tiger Global Management.
Next round funding includes the participation of additional existing investors as well as S Capital VC and TLV Partners, which in total helped them to raise funding of $118 million.
Omri Geller, Run:ai co-founder and CEO has said “We’re thrilled to join NVIDIA and look forward to continuing our journey together.”
He also expressed that Run:ai has been a close collaborator with NVIDIA since 2020 and we are happy to share a passion for helping our customers make the most of their infrastructure.
Run:ai has developed virtualization and orchestration software to make it fit according to the unique needs of AI workloads of GPUs and various chipsets.
Its Kubernetes-based platform for AI clouds effectively helps and shares GPUs by giving the required amount of computing power- ranging from fractions of GPUs, to multiple GPUs, to multiple nodes of GPUs.
Nvidia’s Market Share
Nvidia’s market has raised from a valuation of $1 trillion to $ 2 trillion and is currently running with $2.1 trillion within just nine months of time period.
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Run:ai’s solutions are already coordinated with NVIDIA Base Command, NGC containers, NVIDIA AI Enterprise software, NVIDIA AI Enterprise software, NVIDIA DGX SuperPOD along with its other products.
NVIDIA DGX and DGX Cloud customers will have been provided access to Run:ai’s capabilities to manage their AI workloads, especially for large language model developments.
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