While the AI conversation is often dominated by massive models like ChatGPT and DeepSeek-V3, enterprises should pay closer attention to the infrastructure that powers these innovations – specifically, the silicon that makes AI work at scale.
That means looking beyond the most expensive GPUs, another common fixation, to the broader spectrum of emerging custom silicon already reshaping the AI landscape.
Silicon diversity is becoming critical as AI moves from training to real-world production and edge deployments. Any enterprise that doesn’t embrace it will inevitably fall behind.
Download our latest Emerging Trend Advisory: "Silicon Diverse Clouds: The New Foundation for Modern, Scalable and Sustainable AI" to explore this emerging trend.
Beyond GPUs: Optimizing for business needs, not hype
Most enterprises can’t match the spending patterns of AI labs backed by billions in venture funding – nor do they need to. Few organizations are developing the next GPT-4, and there’s little reason to rely exclusively on NVIDIA’s most expensive GPUs for every workload. The reality is that different AI tasks demand different compute strategies.
While training massive models requires high-end GPUs, inference and edge AI can often run more efficiently on alternative silicon. Arm-based processors, for example, deliver strong performance while reducing cost and power consumption. Similarly, custom AI accelerators optimized for specific tasks can provide better efficiency than a one-size-fits-all approach.
The expanding AI hardware landscape
AI hardware innovation is accelerating, with major players like AMD, Google, and Meta designing specialized processors to meet growing demands. Even within the x86 ecosystem, Intel and AMD are expanding into Arm and RISC-V architectures, giving enterprises more flexibility to match compute options with specific use cases.
This growing diversity means businesses no longer have to chase the latest ultra-powerful GPU. Instead, they should optimize for a balance of performance, efficiency, and cost. Choosing the right mix of compute ensures AI deployments remain scalable without overspending on unnecessary power.
Silicon diversity in the cloud: A smarter approach
As AI infrastructure evolves, enterprises need a cloud provider not locked into a single silicon strategy. A provider with built-in silicon diversity ensures businesses can adapt as new hardware emerges and workloads evolve.
The key is an open, composable cloud model that lets organizations tailor their compute strategy for training, inference, and edge AI without being tied to the constraints of a single vendor.
Learn more about how custom silicon is shaping the future of AI and what you can do to ensure your organization is prepared. Download our latest Emerging Trend Advisory: "Silicon Diverse Clouds: The New Foundation for Modern, Scalable and Sustainable AI" today.