Reserved Capacity
Dedicated GPU capacity can be reserved for your organization through pilot or longer-term agreements.
Managed Dedicated GPU Infrastructure
Dedicated GPU capacity. Fully managed.
Reserve production-grade NVIDIA GPU clusters through focused pilot and reserved-capacity agreements.
We provision, operate, monitor, and support the infrastructure so your team can focus on training, fine-tuning, inference, simulation, and product development.
Start with a dedicated 8-GPU H200 environment, then scale through practical expansion paths as workload demand grows.
Why Link Plumeria
A defined GPU service, operated end to end.
The starting offer is concrete: a dedicated, fully managed H200 environment that can begin as a focused 8-GPU pilot and scale into a larger reserved cluster. Your team gets usable capacity without staffing the infrastructure function.
Dedicated Environments
Clear resource boundaries, consistent access, and control over your operating model.
Managed Operations
We manage provisioning, networking, software, monitoring, maintenance, and support.
Commercial Predictability
Pilot and reserved-capacity agreements give teams clearer planning than upfront purchases or unpredictable consumption spend.
Technical Accountability
Work directly with operators responsible for keeping the environment usable, observable, and ready.
Initial Offering
Managed H200 Cluster
A production-grade managed cluster for AI teams that need reliable access to dedicated GPU capacity without operating the environment themselves.
Engagements can start with a dedicated 8-GPU H200 environment and pilot terms from three months. Larger configurations are available based on workload requirements, availability, and term length.
Service Configuration
Dedicated 8-GPU H200 starting environment
- NVIDIA H200 infrastructure
- 8-GPU dedicated starting configurations
- High-speed GPU interconnect
- Managed Slurm or Kubernetes
- Drivers, CUDA, and NCCL management
- Defined onboarding and acceptance testing
- Monitoring, maintenance, and support
- Hardware diagnosis and replacement
- Direct technical support
- Pilot and reserved-capacity terms with expansion options
Operating Model
Clear responsibility from day one.
A managed cluster works best when the boundary is explicit: Link Plumeria operates the infrastructure, while your team controls the models, data, and application work that runs on it.
Link Plumeria Manages
- Infrastructure operations and GPU networking
- Cluster provisioning and acceptance testing
- Slurm or Kubernetes configuration
- Drivers, CUDA, and NCCL management
- Monitoring, maintenance, and troubleshooting
- Hardware diagnosis and replacement
Customer Manages
- Models, code, and application logic
- Datasets and data governance
- Workload scheduling decisions
- End-user access and permissions
- Experiment design and deployment cadence
- Business priorities for capacity use
How Engagement Works
From capacity discussion to commissioned environment.
The process is designed to move quickly from workload qualification to a commissioned managed environment with defined acceptance criteria.
01
Qualify Demand
Confirm workload type, timing, utilization pattern, storage, network, access, and support requirements.
02
Reserve Capacity
Align on the managed cluster configuration, pilot or reserved term, start timing, and expansion path.
03
Commission Environment
Provision, configure, test, and hand over a usable environment to your technical team.
04
Operate And Support
Monitor, maintain, troubleshoot, and support the environment through the engagement term.
Ideal Fit
Built for AI teams ready for reserved capacity.
A strong fit is a team that has moved beyond intermittent experimentation and can use a dedicated managed environment for production, research, or customer-facing workloads.
CTO or VP Engineering
Add reliable AI infrastructure without assigning engineers to operate the underlying environment.
Head of AI or ML
Move training, fine-tuning, and inference onto capacity planned around your roadmap.
CFO
Replace uncertain procurement and variable consumption exposure with a clearer capacity plan.
- Need for dedicated accelerated compute.
- Workloads that benefit from reliable capacity and consistent access.
- Need for managed operations, monitoring, and support.
- Preference for direct accountability over marketplace-style infrastructure.
- Roadmap that may expand beyond an initial environment.
- Technical team ready to consume infrastructure once delivered.
Next Step
Discuss dedicated GPU capacity.
Tell us what you are trying to run, when you need capacity, and what support model matters. We will respond with an infrastructure approach, availability path, and next steps.