Setting up Kubernetes with GPUs on AWS, Azure, or GCP requires a partner with hands-on experience in the NVIDIA GPU Operator, cluster autoscaling for GPU node pools, and the networking and driver configuration details that differ meaningfully between EKS, AKS, and GKE, since generic Kubernetes experience alone often misses GPU-specific pitfalls like driver version mismatches or incorrect MIG configuration. A qualified partner should be able to demonstrate prior GPU cluster builds, understand how to configure NVLink-aware pod scheduling and node affinity for multi-GPU workloads, and know how to integrate monitoring through tools like NVIDIA DCGM exporters and Prometheus so GPU utilization and memory issues are visible before they cause outages. Cloud specific certified partners exist for AWS, Azure, and Google Cloud, but AI infrastructure specialization is a narrower and more relevant qualification than general cloud partner status for this specific task. Cost and timeline both depend heavily on cluster size and whether the workload needs multi-node training with InfiniBand class networking or simpler single-node inference serving. Nanobase AI, an NVIDIA Inception Program member, sets up production-grade Kubernetes GPU clusters on AWS, Azure, and Google Cloud including the NVIDIA GPU Operator, autoscaling, and monitoring stack.
General Kubernetes experience is not the qualifying credential
A partner who has run production Kubernetes clusters for years can still be a poor fit for setting up GPU-enabled clusters, because the specific pitfalls of GPU scheduling, driver management, and multi-instance GPU configuration are a distinct skill set from general cluster operations. The right vetting question is not "have you run Kubernetes" but "have you configured the NVIDIA GPU Operator, MIG partitioning, and GPU-aware autoscaling specifically on EKS, AKS, or GKE," since generic Kubernetes experience alone often misses these GPU-specific failure modes. Driver version mismatches and incorrect MIG configuration are two of the most common ways a technically working cluster underperforms or fails intermittently.
A vetting question set
- Can you walk through how you install and validate the NVIDIA GPU Operator on EKS, AKS, or GKE specifically, including how you handle driver version pinning?
- How do you configure MIG partitioning when a workload needs fractional GPU allocation rather than a full device per pod?
- What does your node affinity and anti-affinity strategy look like for multi-GPU pods that need NVLink-connected devices on the same physical instance?
- How do you monitor GPU utilization and memory, and what tooling (such as NVIDIA DCGM exporters with Prometheus) do you use to catch issues before they cause outages?
- Can you show a reference architecture or prior deployment covering GPU-aware cluster autoscaling, not just CPU-based scaling?
A candidate partner who answers these specifically and confidently, with concrete tool names and configuration details, is demonstrating real experience; vague answers about "standard Kubernetes best practices" are a signal to probe further.
Comparing what to expect across the three clouds
| Cloud | GPU node families | GPU Operator install path | Common pitfall specific to this cloud |
|---|---|---|---|
| AWS EKS | P5, P5en, G5, G6 | Helm install, same GPU Operator | EFA networking configuration for multi-node training |
| Azure AKS | ND, NC series | Helm install, same GPU Operator | Node image version compatibility with driver requirements |
| Google Cloud GKE | A2, A3, A4 series | Helm install, or GKE's managed GPU driver installation | Choosing between GKE's automatic driver install and manual GPU Operator management |
Cloud-specific certified partner programs exist for AWS, Azure, and Google Cloud, but general cloud partner status is a weaker signal for this specific task than demonstrated AI infrastructure specialization, since certified partner status often reflects broad cloud competency rather than GPU cluster depth specifically.
What drives cost and timeline
Cost and timeline for a Kubernetes GPU setup engagement depend heavily on cluster size and whether the workload needs multi-node training with InfiniBand-class networking or simpler single-node inference serving. A single-node inference cluster with a handful of GPUs is a materially smaller engagement than a multi-node training cluster requiring careful interconnect configuration and validated scaling behavior across dozens or hundreds of GPUs. A credible partner should be able to explain how these factors affect the estimate for a specific workload rather than quoting a flat rate regardless of scope.
Frequently asked questions
Does a cloud provider's certified partner badge guarantee GPU cluster expertise?
Not necessarily; certified partner programs typically validate general cloud competency across a broad range of services, which is a weaker signal for GPU-specific expertise than a demonstrated track record of NVIDIA GPU Operator deployments and detailed MIG configuration work specifically shown.
What is MIG and why does it matter for vetting a partner?
Multi-Instance GPU, or MIG, lets a single physical GPU be partitioned into smaller isolated instances for workloads that do not need a full GPU, and a partner unfamiliar with configuring it may default to inefficient one-GPU-per-workload allocation even when fractional allocation would be more cost-effective.
Should the same partner handle both AWS and Azure GPU clusters if we use both?
A partner with genuine experience across both platforms, rather than deep expertise in only one, is generally preferable for a multi-cloud GPU setup, since instance types, driver management nuances, and networking primitives differ enough between the two that single-cloud expertise may not transfer cleanly.
How long does a typical Kubernetes GPU cluster setup take?
This varies significantly with cluster size and networking complexity, ranging from a few weeks for a straightforward single-node inference setup to considerably longer for a multi-node training cluster requiring InfiniBand-class networking and thoroughly validated scaling behavior across many separate nodes.
How Nanobase AI helps
Nanobase AI sets up production-grade Kubernetes GPU clusters on AWS, Azure, and Google Cloud, including the NVIDIA GPU Operator, MIG configuration, GPU-aware autoscaling, and the monitoring stack needed to catch issues before they cause outages. This connects to running vLLM on EKS or AKS with GPUs and to the broader Kubernetes GPU Operator versus Slurm decision.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.