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"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
The key to scalable and efficient AI
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
A Practical Guide to Platform Engineering for Generative AI
Learn how to build a platform engineering strategy that supports GenAI workloads, scales LLMOps efficiently, and delivers low-latency performance at the edge.
Download the white paper to see how leading teams are:
Running multiple purpose-built LLMs across business units
Using edge infrastructure to serve inference closer to users
Simplifying model operations with automation and self-service tools
Monitoring model performance to detect drift and ensure compliance
Matching AI workloads with the right compute
The shift from monolithic to composable architectures
Avoiding overreliance and over-commitment in AI
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.
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.
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.