#fedml

The innovative platform FEDML Nexus AI, which tackles the growing need for GPU resources and streamlines the creation and implementation of generative AI models, has been released by the rapidly expanding artificial intelligence (AI) startup FEDML. In the quickly changing field of artificial intelligence, developers and businesses looking for effective and affordable solutions can find a wide range of features offered by FEDML Nexus AI.

The GPU Marketplace for AI Development is a vital component of FEDML Nexus AI. The need for GPU resources has increased dramatically as AI models become more advanced and widely used. In response to this need, FEDML Nexus AI offers a large GPU marketplace that includes more than 18,000 compute nodes. These resources are not just for data centers and GPU suppliers; anybody may take part by using the "Share and Earn" interface, which opens it up to a wider audience.

The Unified ML Job Scheduler and GPU Manager provided by FEDML Nexus AI is a special feature. Developers may start AI jobs on the most economical GPU resources with a single command, including training, deployment, and federated learning. This removes the need for environment setup, management, and resource provisioning—all of which take time. Numerous computationally demanding tasks associated with big language models and generative AI are supported by FEDML Launch, including large-scale distributed training, serverless or dedicated deployment endpoints, and large-scale similarity searches in vector databases. It also makes it easier to manage clusters and deploy machine learning workloads in a variety of contexts, such as private clouds, on-premises, and hybrid clouds.

A Zero-code LLM Studio is another aspect of the platform that is very helpful for businesses who want to build bespoke big language models without having to deal with complicated coding. Developers can use FEDML Nexus AI Studio to train, optimize, and implement generative AI models without having to write any code. This makes it possible for businesses to create specialized LLMs using their confidential data in a safe and economical way.

Interest in AI among the enterprise continues to rise, with one recent survey finding that nearly two-thirds of companies plan to increase or maintain their spending on AI and machine learning into this year. But often, these companies are encountering blockers in deploying various forms of AI into production. A 2020 poll from Rexer Analytics found that only 11% of AI models are always deployed.…