AI Companies and Cloud Platforms

AI companies need cloud platforms for several reasons related to scale, performance, flexibility and cost-efficiency.

First of all, all large AI models such as GPT or image-recognition systems require powerful chips (GPUs or TPUs) and computing infrastructure for model training. Cloud providers offer on-demand access to this power. There is no need to invest and maintain costly hardware. Such cloud providers are AWS, Google Cloud and Azure. Besides, cloud environments make it easier to train models across multiple machines or nodes. This accelerates the process.

Secondly, AI models need huge amount of data. Cloud storage allows data storage that is secure, distributed and scalable. It also supports real-time data ingestion and processing.

Thirdly, based on demand, the company’s infrastructure can be scaled up or down. This elasticity helps startups as well as large enterprises.

Fourthly, pay-as-you-go pricing avoids upfront infrastructure investments. It results into cost efficiency,

Since cloud provides security, encryption and compliance with regulations, it is useful while handing data in healthcare or finance.

Clouds have data centers worldwide and the models can be deployed close to users.

Lastly, clouds offer ready-made AI-ML tools.

Microsoft has strategic partnership with OpenAI and provides OpenAI exclusive access to Azure’s supercomputing infrastructure. Microsoft has invested heavily in AI supercomputing infrastructure. It has Nvidia GPUs — A100s or H100s. It has high-speed networking and distributed storage. It has specialised hardware for training LLMs. OpenAI’s models run via APIs running on Azure infrastructure.

Google’s DeepMind uses its own cloud and AI infrastructure, benefiting from TPUs, Borg and Kubernetes systems. It has data storage and inter-connect.

AlphaGO, AlphaFold, Gato and Gemini use Google’s distributed computing and data centres.