Overview
The goal of Rafay's GPU PaaS (Platform as a Service) is to help take a rack of servers and GPUs and convert them into an "End user facing, self-service GPU Cloud" in hours.
Who is it For?¶
Rafay GPU PaaS is designed for both "GPU Cloud Providers" as well as "Enterprises".
What Challenges does it Address?¶
Cloud Providers¶
The primary question every GPU Cloud faces is
How do I go from a rack of GPUs to a revenue generating self service GPU Cloud in days?.
GPU PaaS helps address the following challenges that Cloud Providers encounter.
| Challenge | Description |
|---|---|
| Delayed ROI on Hardware Investments | Time to market issues. Struggling to monetize expensive GPU and hardware infrastructure quickly enough to justify CapEx. |
| Lack of Differentiation in a Crowded Market | Lack of a compelling portfolio of managed AI services and integrated apps. |
| Limited Software-Led Revenue Opportunities | Missing revenue streams from ISV and third-party application ecosystems that could supplement infrastructure income. Lack of a self service experience for users. |
| Margin Pressure Due to High Opex and R&D Spend | High operational complexity (were doing everything manually) and custom engineering efforts (bespoke automation) erode profitability and delay service rollout timelines. |
| Low Platform Stickiness & User Retention | Inability to drive sustained adoption due to limited native AI/ML and GenAI toolsets bundled with compute services. |
Enterprises¶
Due to extreme scarcity of GPUs, every organization is now being forced to
- Make long term commitments to cloud providers, and/or
- Purchase expensive GPUs (e.g. Nvidia H100) ahead of demand, and/or
- Source GPUs from multiple cloud providers
Once they get access to the GPUs, IT/Operations struggle to answer the question:
How do we allocate the GPUs to multiple users in a timely and efficient manner?