CSPs typically move through a structured implementation flow:

• Identify the platform mix – Choose between OxM-supported systems (like Supermicro, Giga Computing, or HPE, amongst many others) or self-managed, in-house builds. This decision sets the foundation for the support, procurement, and deployment models.
• Run a proof of concept – Trial Ampere CPUs using a range of currently available cloud-based instances, bare-metal environments, or partner-delivered systems to validate performance and fit.
• Select a beacon product – Identify a high-impact, low-risk workload that can go into production quickly and demonstrate value. For many CSPs, AI inference is an ideal fit — it's high-visibility, performance-sensitive, and ready to scale. This CSP product could take the form of an Inference-as-a-Service offering for customers or could support a specific inference-based SaaS offering.
• Scale adoption – Once the initial product succeeds, expand Ampere deployment across broader services to realize long-term cost and efficiency gains at even larger scale.

Choosing the right platform upfront ensures a smooth entry point — politically, technically, and operationally. Some CSPs prefer a turnkey solution that integrates with existing OEM or ODM relationships, which is often the fastest path to deployment. Others lean toward full control and customization if they have already built out the platform design, firmware, and support capabilities internally. FAEs can help guide this decision early, helping teams avoid misalignment between procurement strategy and infrastructure goals.

The proof-of-concept phase isn’t about chasing artificial benchmarks — it’s about building trust in real-world performance. Whether trialing through test infrastructure or trial systems, customers use this phase to confirm workload compatibility, power efficiency, and ease of deployment under their actual operating conditions.

A well-chosen beacon product creates momentum. These are practical, production-ready workloads that showcase Ampere’s advantages without requiring a full-stack overhaul. It might be bare metal, IaaS, AI inference, or even DDoS mitigation-as-a-service. Once that first service goes live and delivers, internal buy-in accelerates. It isn’t necessary to move everything to Ampere on Day One.

From there, expansion happens naturally. Some providers extend Ampere into more AI inference offerings, databases, hosting, storage, or a wide variety of other services — all supported by the same efficient, scalable architecture. With lower power consumption and compute-dense hardware, most CSPs quickly realize the total cost of ownership (TCO) gains go far beyond the pilot use case.

As an example, one European CSP began with a single bare-metal deployment. Today, they run hundreds of Ampere-powered instances across multiple customer-facing services. What made the difference wasn’t just performance — it was the ability to scale smart, deliver results, and control infrastructure costs in a power-constrained world.