Enterprises are making daring strikes into AI, and Cisco AI PODs present a robust, pre-validated basis for deploying AI infrastructure at scale. They carry collectively compute, storage, and networking in a modular design that simplifies procurement and deployment. Nonetheless, deploying {hardware} is simply the start. The subsequent essential step is making this highly effective infrastructure consumable as a service.
That is the place Rafay enhances Cisco AI PODs. Rafay’s GPU Platform as a Service (PaaS) provides the essential consumption layer, turning the {hardware} right into a ruled, self-service GPU cloud. Collectively, Cisco and Rafay allow organizations to operationalize AI quicker by providing safe, multi-tenant entry, standardized workload SKUs, and policy-driven governance.
This publish explores how this joint answer transforms uncooked GPU energy right into a production-ready AI platform, enabling developer self-service whereas sustaining enterprise-grade management.
From Infrastructure to Consumption: The Platform Problem
Organizations have accelerated investments in AI infrastructure, deploying platforms like Cisco AI PODs with the newest NVIDIA {hardware} to allow generative AI, Retrieval-Augmented Technology (RAG), and large-scale inference. As adoption grows, a brand new problem emerges: the right way to allow a number of groups to securely and effectively eat this shared infrastructure.
Platform groups should stability entry throughout totally different teams, every with distinctive wants and safety necessities. And not using a standardized consumption layer, this results in a number of issues:
- Underutilized GPUs: Trade benchmarks report common GPU utilization charges typically fall beneath 30%. That is partly as a result of AI workloads are “bursty” and most environments lack the mechanisms to slice and share GPU assets effectively. When costly GPUs sit idle, it represents a big alternative price.
- Handbook Provisioning: Platform groups typically depend on handbook configurations, ad-hoc scripts, and repair tickets to handle entry. These workflows decelerate supply, introduce inconsistencies, and make it tough to implement governance.
- Siloed Sources: And not using a unified platform, GPU infrastructure typically turns into siloed by workforce, limiting sharing and stopping a holistic view of utilization and prices. Builders and researchers should navigate complicated inside processes simply to run a job.
To unravel this, enterprises must function their GPU infrastructure as a service—one which helps shared assets, multitenant isolation, and automatic coverage enforcement.
The Joint Answer: Cisco AI PODs + Rafay GPU PaaS
Cisco and Rafay have collaborated to ship a modular, absolutely validated GPU cloud structure. This answer combines Cisco’s best-in-class AI POD infrastructure with Rafay’s GPU Platform as a Service, remodeling GPU {hardware} right into a safe, self-service, multitenant cloud.
- Cisco AI PODs present the compute, cloth, storage, and pre-validated design. Based mostly on Cisco Validated Designs (CVDs), they combine next-generation Cisco UCS platforms (just like the C885A M8 Server) and the newest NVIDIA GPUs to energy all the AI lifecycle.
- Rafay GPU PaaS delivers the orchestration, coverage enforcement, and developer abstraction layer. It transforms the foundational {hardware} right into a production-grade GPU cloud that’s easy to eat.
This mixed structure allows organizations to quickly launch and function GPU clouds with full-stack orchestration, declarative SKU provisioning, and built-in price attribution.
Developer Self-Service By way of a Curated Catalog
On the core of Rafay’s platform is the SKU Studio, a purpose-built catalog system that empowers platform groups to ship AI-ready infrastructure and purposes as reusable SKUs.
Every SKU is a modular abstraction that bundles:
- Compute Configuration: GPU/MIG profiles, CPU, reminiscence, and storage.
- Utility Stack: Pre-integrated instruments like vLLM, Triton, or Jupyter Notebooks.
- Coverage Controls: Time-to-Reside (TTLs), RBAC, multitenancy, and quotas.
- Billing Metadata: Utilization models and price attribution.
Builders can entry GPU environments immediately by means of a self-service portal (GUI, API, or CLI) without having to file assist tickets. For instance, a knowledge scientist can choose an “H100-Inference-vLLM” SKU, which robotically provisions a particular GPU slice, deploys a safe container, and applies a 48-hour TTL. This streamlines workflows and ensures safety greatest practices are utilized constantly.
Safe Multi-Tenancy and Governance
Sharing costly GPU assets requires strict isolation and governance. Rafay gives native, safe multi-tenancy that enables groups to securely share infrastructure with out interference.
Key safety controls are robotically enforced:
- Hierarchical RBAC: Defines permissions and entry scope for tenants, initiatives, and workspaces.
- Namespace Isolation: Ensures workloads are separated on the cluster and community stage.
- Useful resource Quotas: Prevents any single workforce or job from monopolizing assets.
- Centralized Audit Logs: Gives an entire audit path of person actions for compliance.
These built-in protections permit platform groups to keep up full oversight and management whereas empowering builders with the liberty they should innovate.
Complete GPU Administration and Visibility
To maximise ROI, you’ll want to know the way your GPUs are getting used. Rafay gives end-to-end visibility, metering, and price attribution tailor-made for multitenant environments.
Platform groups can use declarative blueprints to standardize GPU operator configurations and slicing methods (like MIG) throughout all clusters. Multi-tenant dashboards supply detailed insights into:
- GPU stock and allocation
- SKU utilization patterns
- Occasion-level exercise and person attribution
- Well being standing and uptime developments
A billing metrics API aggregates utilization information, calculates billable compute, and generates auditable studies, enabling chargebacks and monetary accountability.
Who Advantages from a Unified GPU Cloud?
This collectively validated answer is designed for a various vary of consumers who must operationalize GPU infrastructure with safety, velocity, and scale.
- Enterprise IT Groups: Achieve federated self-service, quota enforcement, and centralized visibility. This reduces infrastructure duplication and embeds governance into day by day operations.
- Sovereign & Public Sector Organizations: Meet compliance wants in air-gapped environments with safe multitenancy, coverage enforcement, and centralized audit logging.
- Cloud & Managed Service Suppliers: Monetize GPU infrastructure with a white-labeled, multitenant platform that features automated tenant onboarding and built-in chargeback metering.
- Present Cisco Clients: Prolong the ROI of present UCS deployments by including GPU orchestration as a seamless overlay with no re-architecture required.
- Greenfield AI Builders: Begin contemporary with a pre-validated, absolutely built-in answer that reduces the time from procurement to operational AI companies from months to weeks.
Operationalize Your AI Infrastructure At the moment
Pairing Cisco’s validated AI infrastructure with Rafay’s GPU PaaS management aircraft permits organizations to remodel GPU programs into absolutely ruled inside platforms. The result’s a consumption-driven structure the place builders achieve self-service entry, operators implement quotas and monitor consumption, and the enterprise maximizes the worth of its AI investments.
This structure presents a transparent path ahead: ship GPU infrastructure as a service, allow safe and compliant multitenancy, and make consumption predictable and cost-aligned from day one.
To see this highly effective answer in motion, be a part of our upcoming webinar. Specialists from Cisco and Rafay will exhibit the right way to remodel your GPU infrastructure right into a production-ready AI service.
Reside Webinar: From AI PODs to GPU Cloud
October 21, 2025 at 8:00 a.m. PST / 3:00 p.m. GMT
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