Enterprises are making daring strikes into AI, and Cisco AI PODs present a strong, pre-validated basis for deploying AI infrastructure at scale. They create collectively compute, storage, and networking in a modular design that simplifies procurement and deployment. Nevertheless, deploying {hardware} is barely 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 sooner by providing safe, multi-tenant entry, standardized workload SKUs, and policy-driven governance.
This submit 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 way to allow a number of groups to securely and effectively eat this shared infrastructure.
Platform groups should stability entry throughout completely 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: Business benchmarks report common GPU utilization charges usually fall under 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 major alternative price.
- Handbook Provisioning: Platform groups usually 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 usually turns into siloed by crew, limiting sharing and stopping a holistic view of utilization and prices. Builders and researchers should navigate advanced inside processes simply to run a job.
To resolve this, enterprises have to 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, material, storage, and pre-validated design. Primarily based 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 the whole 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 permits organizations to quickly launch and function GPU clouds with full-stack orchestration, declarative SKU provisioning, and built-in price attribution.
Developer Self-Service By a Curated Catalog
On the core of Rafay’s platform is the SLA STUDIOa 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-Stay (TTLs), RBAC, multitenancy, and quotas.
- Billing Metadata: Utilization items and value attribution.
Builders can entry GPU environments immediately by a self-service portal (GUI, API, or CLI) without having to file help tickets. For instance, an information scientist can choose an “H100-Inference-vLLM” SKU, which mechanically 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 persistently.
Safe Multi-Tenancy and Governance
Sharing costly GPU assets requires strict isolation and governance. Rafay gives native, safe multi-tenancy that permits groups to securely share infrastructure with out interference.
Key safety controls are mechanically enforced:
- Hierarchical RBAC: Defines permissions and entry scope for tenants, initiatives, and workspaces.
- Namespace Isolation: Ensures workloads are separated on the cluster and community degree.
- Useful resource Quotas: Prevents any single crew or job from monopolizing assets.
- Centralized Audit Logs: Supplies a whole audit path of person actions for compliance.
These built-in protections permit platform groups to take care of full oversight and management whereas empowering builders with the liberty they should innovate.
Complete GPU Administration and Visibility
To maximise ROI, you might want to understand how your GPUs are getting used. Rafay gives end-to-end visibility, metering, and value 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 tendencies
A billing metrics API aggregates utilization information, calculates billable compute, and generates auditable reviews, enabling chargebacks and monetary accountability.
Who Advantages from a Unified GPU Cloud?
This collectively validated answer is designed for a various vary of shoppers who have to operationalize GPU infrastructure with safety, velocity, and scale.
- Enterprise IT Groups: Acquire federated self-service, quota enforcement, and centralized visibility. This reduces infrastructure duplication and embeds governance into every 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 providers from months to weeks.
Operationalize Your AI Infrastructure Right now
Pairing Cisco’s validated AI infrastructure with Rafay’s GPU PaaS management airplane permits organizations to remodel GPU programs into absolutely ruled inside platforms. The result’s a consumption-driven structure the place builders acquire self-service entry, operators implement quotas and monitor consumption, and the enterprise maximizes the worth of its AI investments.
This structure affords 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 display the way to remodel your GPU infrastructure right into a production-ready AI service.
Stay 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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