Silicon Diverse Clouds
Unlocking enterprise AI’s full potential through distributed inference
The New Foundation for Modern, Scalable and Sustainable AI
As AI accelerates, enterprises are leveraging silicon diversity – specialized chips for different workloads – to maximize performance and stay competitive.
The key to scalable and efficient AI
Matching AI workloads with the right compute
- AI success depends on precisely matching workloads with the most efficient compute resources, whether CPUs, GPUs, or specialized accelerators.
- AI-first clouds provide the flexibility to seamlessly integrate emerging hardware, ensuring that enterprises stay ahead in a rapidly-evolving AI landscape.
Meet us at Cloud Expo Asia and explore how Vultr’s composable cloud solutions can empower your business to grow, innovate, and scale.
"By 2027, 40% of existing AI data centers will be operationally constrained by power availability, and by 2028 more than 25% of new servers will include dedicated workload accelerators to support GenAI workloads." – Gartner
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Tel Aviv AI Summit 2024
Why AI agents are essential and how your company can get in the game
Read our whitepaper, “Prepare Your Enterprise for Agentic AI, the Next Tech Evolution Beyond Generative AI,” to discover how enterprise teams can enable AI agents driving the next wave of enterprise automation.
Learn about unlocking AI performance with the power of silicon diversity
Dive into Vultr’s latest whitepaper
As AI accelerates, enterprises are leveraging silicon diversity – specialized chips for different workloads – to maximize performance and stay competitive.
"By 2028, 33 percent of enterprise software applications will include agentic AI, up from less than 1 percent in 2024."
- Gartner
Generative and agentic AI are spawning a new, edge-based infrastructure operating paradigm
- Over 80% of AI and IT professionals expect to grow their AI edge operations in the coming year
- The emergence of agentic AI multiplies the magnitude of the architectural and operational shift organizations are already making in pursuit of scaling inference at the edge
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The shift from monolithic to composable architectures
- Traditional, one-size-fits-all architectures can’t keep pace with AI’s growing complexity – specialized silicon delivers performance for workloads.
- From GPUs to domain-specific accelerators, silicon diversity optimizes AI training and inference, enabling faster, more scalable AI applications.
Meet us at Cloud Expo Asia and explore how Vultr’s composable cloud solutions can empower your business to grow, innovate, and scale.
Avoiding overreliance and over-commitment in AI
- Relying too heavily on traditional hyperscalers or proprietary GPUs can significantly drive up costs while restricting your ability to adapt and scale efficiently.
- AI-first clouds allow you to choose the best infrastructure for your needs, offering cost control, scalability, and access to cutting-edge compute without lock-in.
Meet us at Cloud Expo Asia and explore how Vultr’s composable cloud solutions can empower your business to grow, innovate, and scale.
Serverless inference is the only path forward as AI innovation accelerates
- The demand for compute resources to support edge-based inference to execute transactions by the immense number of AI agents is increasing exponentially
- The vast majority of enterprises cannot afford the cost of procuring and maintaining the array of GPU and CPU resources needed to support GenAI and agentic AI at scale
Agentic AI will grow exponentially in the next few years, bringing major disruption – and opportunity
- While only 10% of organizations currently use AI agents, over 50% plan to adopt them in the next year; within 3 years, 82% of enterprises plan to have them incorporated (Source: Capgemini)
- The number of AI agents will surpass the number of humans on Earth in the next 2-3 years (Source: Jeremiah Owang – Former Forrester analyst turned venture capitalist)
Dive into Vultr’s latest whitepaper
Explore insights and strategies for how your organization can navigate the technological shift towards agentic AI and build a genuinely future-proof AI infrastructure foundation.