GPU-as-a-Service
The right GPUs for every workload, production ready.
Run AI workloads on infrastructure powered by AMD Instinct GPUs, available through bare metal, managed Kubernetes or on-demand options and operated by Rackspace 24x7x365.
Getting GPUs is hard. Keeping them productive is harder.
Consumption models
Three routes to GPU capacity, one operator behind them all
Bare metal
Dedicated GPU servers for production workloads that require consistent performance, physical isolation and control.
Managed Kubernetes for AI
A managed container platform for deploying AI workloads on GPU infrastructure with consistent operations.
On Demand
Self-service GPU capacity for development, proofs of concept and bursts, without a long-term capacity commitment.
Workload capacity that earns its keep.
Right-sized
GPU options support workloads from edge and small-model inference to large-model inference and fine-tuning.
Governed by default
GPUs run in-region, in sovereign configurations or in your own facility, and your data never trains external models.
Open by design
Maintain your choice of AI models and frameworks as workload requirements change.
Operated end to end
Rackspace manages monitoring, drivers, firmware, lifecycle and capacity planning across the GPU environment.
Kept productive
Scheduling and capacity management help align GPU resources with workload demand and reduce idle capacity.
Connected to the stack
Connect GPU infrastructure with managed inference, fine-tuning and data engineering with the same team.
RACKSPACE & AMD
AMD compute, matched to your AI workloads' demands
AMD is a strategic technology partner at the silicon layer of the Rackspace governed AI stack. AMD Instinct GPUs and AMD EPYC CPUs provide accelerated and general-purpose compute within an integrated Enterprise AI Cloud architecture, with Rackspace matching each workload to compute based on its performance, cost and control requirements.
Workloads
GPU-powered compute for your most demanding AI workloads.
Inference at scale
Language, vision and multimodal models serving applications and agents.
Fine-tuning
Smaller domain models trained on your data for better accuracy and fewer tokens.
Search and retrieval
Embedding and retrieval pipelines grounded in enterprise content.
Agents
Agentic workloads that call models repeatedly and need capacity that keeps pace.
Clinical and regulated AI
Imaging, summarization and document workloads on data that stays governed.
AI at the edge
Inference close to where data is created, in hospitals, branches and plants.
Operating model
Build. Operate. Innovate.
GPU capacity only pays off when it is sized to the work, run every day by people who own the outcome, and refreshed as the hardware and software move. One team does all three.
Build
Sized and deployed
Capacity planned around your workloads, then provisioned and tested by the team that will run it.
Operate
Run like it's our own
GPU health, scheduling, security and lifecycle handled every day, with one team to call.
Innovate
Next generation, in place
New accelerators and software brought in as they prove out, so capacity keeps pace with your models.
Branchen
Bewährte Expertise für Branchen, in denen es um das Höchste geht
Healthcare
Epic and clinical platforms kept available, patient data protected, and a governed foundation ready to move AI from pilot to the point of care.
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Financial services
Core banking and payments modernized with proven recovery, data control and the audit evidence regulators expect, so AI can follow safely.
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Public sector
Sovereign, accredited cloud for citizen services, run by cleared teams, with the control to modernize today and adopt AI when you’re ready.
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CONTACT US
GPU capacity, matched to your AI workload needs.
FAQ
Common questions about GPU-as-a-Service
What is GPU-as-a-Service?
GPU-as-a-Service (GPUaaS) is a cloud model that gives organizations access to graphics processing units (GPUs) for AI, machine learning and other compute-intensive workloads without buying or maintaining the hardware. Capacity is typically offered on demand, as dedicated bare metal servers or through a managed Kubernetes platform, and billed by usage or commitment. It lets teams train, fine-tune and run AI models faster and scale capacity as needs change. Rackspace GPU-as-a-Service delivers AMD Instinct GPU capacity on governed infrastructure, operated by Rackspace 24x7x365.
What is Rackspace GPU-as-a-Service?
Rackspace GPU-as-a-Service provides AMD Instinct GPU infrastructure for AI workloads through bare metal, managed Kubernetes or on-demand options, with governed infrastructure operated by Rackspace.
Which GPUs does Rackspace GPU as a Service use?
Rackspace GPU as a Service is built on AMD Instinct GPUs alongside AMD EPYC CPUs. AMD is a strategic technology partner at the silicon layer of the Rackspace governed AI stack, with 30 MW of AMD AI infrastructure planned for phased deployment.
Which GPU consumption models are available?
Three: bare metal GPU servers for production workloads that require consistent performance, isolation and control; managed Kubernetes for containerized AI applications; and on-demand GPU capacity for development, proofs of concept and workload bursts without a long-term capacity commitment.
Can GPU-as-a-Service run in private or sovereign environments?
Yes. GPU workloads can run in-region, in sovereign configurations or in customer facilities to meet data, location and control requirements.
Typically with an AI readiness assessment or a cloud strategy assessment, followed by a working session that produces a prioritized plan with business cases. From there, Rackspace can prove a first solution, take it to production and operate it.