Simplify your AI data pipeline
Getting an end-to-end view of your data and turning your AI strategy into a pipeline of well-planned projects is critical to delivering on their promise. Going from proof of concept, right through to production, turning all the data within your business into a strategic asset with AI, requires a new way of thinking about how you manage that pipeline throughout its lifecycle.
Train and tune AI models faster
Time to market is challenging with AI. Training and tuning models faster requires you to bring together the software, supercomputing technologies and expertise that will allow your models to continuously adapt and learn autonomously, at scale.
Make your AI sustainable from the start
Large AI models require massive computing power and energy to train. For instance, in 2020, according to recent studies, it took 1,287 MWh to train GPT-3
Accelerate generative AI and large language models (LLM)
The promise of generative AI is beginning to be realized and will be transformational in advancing the way we live and work. But the models are huge and the data just keeps on growing. Accelerating the development of such large language models requires the removal of complexity and barriers to entry, in an optimal environment uniquely architected for AI.
HPE enters the AI cloud market: introducing HPE GreenLake for Large Language Models
On-demand, multi-tenant cloud service provides enterprises the power to privately train, tune, and deploy large-scale AI models.