GPU Worker Node NVIDIA

High-efficiency Kubernetes compute with NVIDIA GPU

GPU Worker Node (NVIDIA)
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Overview

Bridge the gap between your Kubernetes clusters and next generation AI with GPU NVIDIA worker nodes. Powered by NVIDIA H200 NVL, these nodes deliver up to 30 TFLOPS (FP64), 60 TFLOPS (FP32), 1,671 TFLOPS (FP16), and 3,341 TFLOPS (FP8) with 141 GB of ultra-fast HBM3e memory. Designed for LLM/Deep learning-based model training and inferencing, NVIDIA GPU worker nodes integrate with Kubernetes for native resource scheduling, pod-level GPU allocation.

With robust reliability, automatic node scaling, and instant provisioning, you can deploy GPU-intensive applications directly into your clusters without infrastructure changes. Leverage container-native GPU scheduling, resource isolation, and workload orchestration to build next-generation AI applications while maintaining operational consistency across your cloud-native stack.

Pricing

To know more about the SKUs and pricing click below.

Core Features at a Glance 

Native Kubernetes integration
Seamlessly add NVIDIA GPU compute with automatic node registration, label assignment, and resource advertisement through NVIDIA device plugin.
Pod-level GPU scheduling
Request GPU resources via standard Kubernetes specifications, with support for precise workload placement across cluster.
Extreme model capacity
Train and run large LLM/ML models with 141 GB of ultra-fast HBM3e memory per GPU and bandwidth up to 4.8 TB/s (dual-GPU with NVLink), supporting extended context lengths and efficient parallel processing.
Optimized precision modes
Support FP8, BF16, FP16, INT8, as well as FP32 and FP64 Tensor Core precision for efficient and flexible AI compute with reduced memory usage.
Container-native deployment
Deploy GPU workloads using standard Kubernetes manifests with automatic NVIDIA GPU Operator, CUDA libraries access, and container runtime optimization.
Multi-GPU pod support
Scale pods across 1, 2, 4, or 8 GPUs per pod with NVLink interconnect for distributed training and high-throughput inference.
Cluster autoscaling
Automatically scale GPU worker nodes based on resource demands, for optimal resource utilization and cost efficiency.
Framework-ready environment
Full compatibility with NVIDIA CUDA and leading AI/ML frameworks like PyTorch, TensorFlow, and Hugging Face Transformers making it immediate container deployment ready.

What You Get

Kubernetes-native GPU nodes
NVIDIA H200 NVL worker nodes with automatic registration, tainting, and resource advertisement.
Pre-configured GPU runtime
Optimized container runtime, NVIDIA device plugin, GPU drivers, and CUDA libraries automatically configured for immediate deployment.
Enterprise-grade orchestration
Kubernetes-native high availability, pod security policies, network isolation, and persistent storage support for running production GPU workloads.
Seamless cluster integration
Add GPU capacity without disrupting existing workloads, with support for mixed CPU/GPU cluster support with intelligent workload placement.
Monitoring and observability
GPU utilization metrics through Kubernetes monitoring stack, resource quotas, performance dashboards, and API access for automation.

Still have questions?

NVIDIA GPU worker nodes automatically join clusters via kubeadm/kubelet, with the NVIDIA device plugin exposing GPU resources through the Kubernetes API.
LLM inference pods, distributed training, batch processing workloads, and recommendation system deployments that require high memory bandwidth with multi-GPU coordination are best suited for H200 NVL.
H200 NVL worker nodes provide 141 GB HBM3e memory per GPU with ~4.8 TB/s bandwidth in dual GPU NVLink configurations.
Yes, GPU worker nodes are tainted with nvidia.com/gpu:NoSchedule to block non-GPU workloads and support standard scheduling features.
Pods request GPUs using nvidia.com/gpu resource limits, and the Kubernetes scheduler automatically places them on suitable GPU worker nodes.
Individual pods can request 1, 2, 4, or 8 GPUs with automatic scheduling depending on workload requirements and resource availability.
All major frameworks – including TensorFlow, PyTorch, JAX, and ONNX – run seamlessly through containerized deployments with Kubernetes manifests and CUDA access.
Yes, we provide ready-to-deploy images with pre-installed drivers, CUDA libraries, and ML frameworks optimized for NIVDIA GPU worker nodes.

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