NVIDIA Expands Cloud Business with Investments, Partnerships
Hugging Face is steadily growing to be the one stop solution for AI models, after their partnership with NVIDIA, even more so. Recently, they announced that Hugging Face users will have access to NVIDIA DGX Cloud AI supercomputing to train and fine tune their AI models. With this partnership, Hugging Face users get access to SOTA GPUs and infrastructure needed to rapidly train and finetune foundation models at scale and drive a new wave of enterprise LLM development. Hugging Face has divided the costs of building specific models parameters on DGX, tokens and datasets from the range of $32,902 to $18,461,354 making the process more efficient. “We hope to spur a new wave of experimentation and learning in AI – exploration that simply wasn’t feasible before,” said Julien Chaumond. NVIDIA CEO Jensen Huang also acknowledged the immense potential of Hugging Face as an AI enabler and said, “I think there are 50,000 companies with 2 million users and there’s some 275,000 models and 50,000 datasets. Just about everybody who creates an AI model and wants to share it with the community puts it up on Hugging Face.” NVIDIA, now a stakeholder at hugging face, is further driving their own product through them. They are likely to reap the benefits in revenue this year and next because of massive customer investments. While Hugging Face got a big boost from this partnership, NVIDIA has another plan. They’re growing the user base of their DGX cloud by inviting the open source community and accelerating AI innovation at scale. Is NVIDIA Eating Cloud? NVIDIA has positioned themselves smartly between the cloud service providers and their customers. In March, NVIDIA announced that the company  is partnering with leading cloud service providers to host DGX Cloud infrastructure, starting with Oracle Cloud Infrastructure (OCI). The company also said that Microsoft Azure, Google Cloud will Host DGX Cloud soon. Because of the partnership, users get to quickly access GPU servers and DGX Cloud without having to make commitments to multiple cloud vendors. While NVIDIA insists this partnership is a shared success, it is clearly at odds with other cloud providers. The use of DGX as a “single software platform” from NVIDIA allows companies to streamline the operation of its new AI software across various cloud providers and within its own data centres, enhancing efficiency. Additionally, NVIDIA’s DGX cloud servers are built by engineers who leverage their knowledge of the company’s chips and are in a better position to fine-tune DGX Cloud servers. As expected, this surpasses their performance in comparison to other AI-centric servers rented out by cloud providers, as confirmed by individuals closely acquainted with the service. Interestingly, the similar partnership offer by NVIDIA was also made to  AWS, but it refused. Joshua Bernstein, a former manager at AWS and Google Cloud, commenting on this said, “It puts NVIDIA’s brand front and centre over a cloud providers’ brand.” AWS is the biggest player in the cloud sector and is already a formidable competitor with their EC2 P5 service. NVIDIA for Startups Nvidia is slowly building up its foothold in the AI startup and enterprise ecosystem. The startups themselves might be slow in arrival but NVIDIA is making sure to enable them. Last year, NVIDIA open-sourced certain parts of their GPU software for Linux after criticism of not being open-source friendly. They also said that they’re making code run more efficiently across different types of processors like CPUs, GPUs, and AI accelerators. To support the open source projects, they have a team of engineers for support and build of their services.  This inevitably helps startups and enterprises to build on top of their services as a comprehensive solution. Apart from their DGX Cloud capabilities, NVIDIA has a host of cloud products and services at a reasonable cost for enterprises. They unveiled a suite of cloud-based services tailored to develop generative AI models specialised for specific tasks within their domains, such as medical imaging. These services fall under the umbrella of NVIDIA AI Foundations and encompass two distinct offerings: NVIDIA NeMo, dedicated to language models, and NVIDIA Picasso, geared towards generating image, video, and 3D content. Once these models are prepared for deployment, businesses have the flexibility to run them either within NVIDIA’s cloud infrastructure or on other platforms of their choice. Source: NVIDIA NeMo NVIDIA is also heavily investing in AI startups. They’ve invested in 20+ companies this year apart from Hugging Face. The most recent was yesterday, when Databricks announced it received funding from NVIDIA and Capital One. These agreements enable Nvidia to keep AI companies loyal to their products. Majority of these startups and enterprises are already Nvidia patrons, from which the company stands to regain its investments.
With NVIDIA partnership, Hugging Face users get access to SOTA GPUs and infrastructure needed to rapidly train and finetune foundation models at scale and drive a new wave of enterprise LLM development.
With NVIDIA partnership, Hugging Face users get access to SOTA GPUs and infrastructure needed to rapidly train and finetune foundation models at scale and drive a new wave of enterprise LLM development. 
["AWS", "Funding", "Google Cloud", "GPUs", "Hugging Face", "investments", "Microsoft Azure", "NVIDIA", "Oracle", "partnership"]
["partnership", "Hugging Face", "Funding", "Google Cloud", "AWS", "investments", "AI", "R", "ML", "Oracle", "RAG", "generative AI", "Microsoft Azure", "GPUs", "NVIDIA", "Azure", "foundation models", "Databricks"]
["AI", "ML", "generative AI", "foundation models", "Hugging Face", "RAG", "AWS", "Azure", "Databricks", "R"]
K L Krithika is a tech journalist at AIM. Apart from writing tech news, she enjoys reading sci-fi and pondering the impossible technologies, trying not to confuse it with reality.