GPU Console

A powerful Infrastructure-as-a-Service (IaaS) GPU Console for direct GPU rental and bare-metal server operations, designed to empower developers and enterprises with unparalleled access to decentralized computing resources.

The Planck GPU Console puts the raw power of our decentralized GPU network at your fingertips. Whether you're a machine learning engineer, a data scientist, or a company seeking scalable compute solutions, our console provides the tools you need to build, train, and deploy your AI and high-performance computing workloads.

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Virtual Machines (VMs) with NVIDIA GPUs:

• Effortless Deployment: Spin up virtual machines equipped with high-performance NVIDIA GPUs in minutes.

• Customizable Configurations: Choose from a variety of GPU models and VM configurations to match your specific workload requirements.

• Example: Imagine you're a researcher training a large language model. You can quickly deploy a VM with multiple H100 or A100 GPUs (lower-grade GPUs are also in stock), install your preferred deep learning framework (TensorFlow, PyTorch), and begin training immediately. You have root access and full control over your VM environment.

• Use Case: Ideal for individual developers and small teams needing flexible and scalable GPU resources for AI/ML development, simulation, and rendering.

GPU Clusters with H100s and H200s:

• Scalable Performance: Create and configure GPU clusters with cutting-edge NVIDIA H100 and H200 GPUs for demanding workloads.

• High-Bandwidth Interconnects: Leverage high-speed network interconnects between GPUs for optimal performance in distributed training and large-scale simulations.

• Example: A large AI company needs to train a massive recommendation system. They can create a GPU cluster with hundreds of H100s, distribute their training workload across the cluster using frameworks like Horovod or DeepSpeed, and significantly reduce training time.

• Use Case: Perfect for enterprises and research institutions requiring massive compute power for large-scale AI training, scientific simulations, and high-performance computing applications.

Managed Kubernetes Clusters:

• Containerized Workloads: Create and manage Kubernetes clusters for deploying and scaling containerized applications.

• Simplified Deployment: Streamline the deployment and management of your AI/ML applications using container orchestration.

• Example: A startup developing a cloud-based AI service can deploy their application as a set of Docker containers on a managed Kubernetes cluster. This allows them to easily scale their service based on demand and manage updates with zero downtime.

• Use Case: Ideal for teams deploying microservices-based AI applications, enabling efficient resource utilization and simplified application management.

Managed Ray Clusters:

• Scalable Machine Learning: Create managed single and mega Ray clusters to run distributed machine learning operations.

• Simplified Distributed Training: Leverage Ray's powerful capabilities for distributed training, hyperparameter tuning, and reinforcement learning.

• Example: A data science team needs to perform hyperparameter tuning for a complex machine learning model. They can create a managed Ray cluster, distribute the tuning process across multiple nodes, and significantly accelerate the search for optimal hyperparameters.

• Use Case: Ideal for data scientists and machine learning engineers working on large-scale machine learning projects requiring distributed computing capabilities.

Object Storage:

• Scalable Data Storage: Create object storage buckets for storing data in any format, from raw data to trained models.

• Secure and Reliable: Benefit from secure and reliable data storage with high availability and durability.

• Example: A research team collecting large datasets from sensors can store their data in an object storage bucket. They can then access and process the data using their GPU VMs or clusters.

• Use Case: Essential for storing large datasets, trained models, and other AI/ML artifacts, providing a central repository for data management.

Planck GPU Console allows you to:

• Access Decentralized Compute: Tap into a vast network of GPUs, reducing reliance on centralized cloud providers.

• Scale On-Demand: Quickly scale your compute resources to meet your evolving workload demands.

• Optimize Costs: Benefit from competitive pricing and pay-as-you-go billing.

• Accelerate Innovation: Focus on your AI/ML development, not infrastructure management.

Get started with Planck GPU Console here