Meeting growing demands of GenAI workloads with CentML
How a single-platform adoption approach streamlines AI app development and deployment
Broadcast: June 4th, 2025, Wednesday | 9 AM PST | 12 PM EST
Since the launch of ChatGPT in 2022, GenAI has reshaped industries and opened new possibilities for operational innovation. In McKinsey's latest ‘State of AI’ survey, 78 percent of respondents report their organisations use AI in at least one business function – a 50% increase since 2023. This rate of growth that places unprecedented pressure on researchers, developers and IT leaders to continually optimise their AI projects.
But for many businesses, adopting GenAI remains challenging. High costs, complex deployments, high compute resource requirements, and a rapidly-evolving ecosystem hamper widespread adoption and create backlogs in project delivery. As businesses grow more AI-driven, the need to control the resources that AI deployment consumes also increases.
To keep pace with this rapid rate of change, developers need an all-in-one solution for scalable AI deployment. The CentML Platform is a secure, full stack solution for AI development and rollout. It provides a frictionless and economical AI deployment solution for both enterprises and startups.
With the CentML Platform, organisations can focus on developing AI applications without worrying about optimising infrastructure for large-scale deployments – be they on CentML-hosted infrastructure or on proprietary GPU clusters.
Join this Register webinar in which John Palazza, VP of Sales at CentML, explains how, with the CentML Platform, business organisations can:
Deploy custom models or choose from a catalogue of CentML-optimised open source LLMs on a wide range of GPU options.
Deploy models on proprietary infrastructure, whether on-premises GPU clusters or dedicated Virtual Private Clouds.
Efficiently manage, scale and orchestrate resources through job scheduling, auto-scaling, traffic control, and real-time monitoring.
Enable developers to preview performance across cost, latency and throughput dimensions.