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GPU CONSULTANCY

Your GPUs. Your cluster. A clear plan forward.

Get expert advice and end-to-end GPU cluster setup with Kubernetes. From planning your hardware to deploying workloads, gpufleet helps you build, configure, and operate your cluster in the cloud or on premises.

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GUIDANCE FOR YOUR GPU INFRASTRUCTURE

Build your GPU strategy around your workloads.

Choosing GPUs is only one part of building a useful AI platform. Memory requirements, networking, storage, scheduling, and workload demand all shape the result. We review your goals and environment to identify constraints and prioritize practical improvements.

WHAT IT ENABLES
  • A GPU and capacity plan aligned with your training, inference, or development workloads.
  • A Kubernetes GPU cluster configured from initial setup through workload deployment and validation.
  • Cluster architecture recommendations covering compute, networking, storage, and workload scheduling.
  • Prioritized opportunities to improve utilization, reliability, and infrastructure costs.
HOW WE APPROACH IT
  • Start with your workload requirements, existing GPUs, budget, and growth plans.
  • Set up Kubernetes, integrate the NVIDIA GPU software stack, and configure networking, storage, and access controls.
  • Review cluster configuration, utilization data, and performance bottlenecks with your team.
  • Deliver recommendations and an implementation roadmap with clear tradeoffs and next steps.
END-TO-END CLUSTER SETUP

From GPU servers to a working Kubernetes cluster.

We help configure the full platform, test it with your workloads, and prepare your team to run it.

01

Plan and prepare

Define workload and capacity requirements, select GPU servers or cloud instances, and plan the cluster architecture, networking, and storage.

02

Install Kubernetes

Set up the Kubernetes control plane and worker nodes, connect GPU nodes, and configure cluster networking, persistent storage, and access controls.

03

Configure GPU access

Integrate the NVIDIA GPU Operator, drivers, container toolkit, and device plugin with your environment. Configure GPU scheduling and MIG or time-slicing where appropriate.

04

Deploy and validate workloads

Deploy your training or inference workloads, verify GPU access, and test networking, storage, and application performance against agreed requirements.

05

Set up monitoring

Configure GPU and cluster metrics, dashboards, and alerts so your team can track utilization, identify failures, and investigate performance issues.

06

Handover and ongoing support

Document the configuration and operating procedures, walk your team through the platform, and plan upgrades, maintenance, and ongoing support around your needs.

HOW WE CAN HELP

From your first GPU to an established cluster.

01

Plan your GPU infrastructure

Evaluate GPU memory and compute needs, cloud and on-premises options, network and storage requirements, and capacity for future workloads.

02

Improve an existing cluster

Investigate idle GPUs, workload bottlenecks, scheduling issues, and operational gaps. Assess whether MIG, time-slicing, or dedicated GPUs fit your workloads.

03

Turn advice into next steps

Work through recommendations with your engineers, implement agreed changes, and measure their impact. Get hands-on support from initial setup through cluster handover.

CONTACT

Build a GPU platform that works for your workloads.

We help you select, configure, and operate the Kubernetes GPU capabilities your team needs.

Talk to gpufleet →