Cloud GPU-powered compute and Kubernetes

Choosing the right GPU among the NVIDIA L40S, H100, and GH200 Grace Hopper depends on your specific needs and your use case:

• NVIDIA A100: Delivering unmatched AI and graphics performance with cutting-edge technology, the NVIDIA A100 is the pinnacle of innovation for demanding workloads in AI, data analytics, and scientific computing.
• NVIDIA L40S: Ideal for those who need a balanced blend of AI and graphics performance. Powered by the NVIDIA Ada Lovelace architecture with 48 GB GDDR6 memory, it's particularly well-suited for tasks that require both intricate AI computations and advanced graphics processing, like 3D graphics, rendering, and large language model training.
• NVIDIA H100: Best suited for intensive AI training and inference tasks. It offers options of 4GB or 80GB HBM2e memory and is equipped with fourth-generation Tensor Cores, making it a superior choice for developing and deploying large AI models like chatbots and recommendation engines. The H100 is also designed for scalable multi-GPU systems, ensuring high performance and robust security for data center operations.
• NVIDIA GH200 Grace Hopper™ Superchip: The go-to choice for cutting-edge AI and high-performance computing (HPC) workloads. It features an integrated CPU-GPU architecture with a high-bandwidth NVLink C2C interconnect, enhancing performance for AI and HPC tasks. With its 72-core Grace CPU, H100 Tensor Core GPU, and up to 624GB of memory, it's tailored for generative AI, large-scale AI inference, or scientific computing that demands the highest level of memory and processing power.

Your choice should be based on the specific balance of AI, graphics, and HPC needs in your projects, considering factors like memory requirements, processing power, and the nature of the tasks (AI training/inference, graphics rendering, HPC tasks, etc.).

At Civo, we are dedicated to providing climate-friendly GPU products. Our sustainable cloud solution, powered by 100% renewable energy, runs on Deep Green's carbon-neutral data center. We effectively capture 90% of the heat generated by our servers, repurposing it for community-based projects, thus reducing our carbon footprint and contributing to a sustainable future.

As the exclusive cloud partner of Deep Green, we offer state-of-the-art GPU-powered servers that align with environmental responsibility. We believe in making sustainability accessible; hence, we've maintained competitive pricing across all regions to encourage more businesses to adopt eco-friendly computing solutions.

Yes, the NVIDIA L40S, H100, and H200 Grace Hopper GPUs can be scaled for larger projects. These GPUs are designed with scalability in mind, allowing them to be integrated into multi-GPU systems. This capability is crucial for handling more complex or larger-scale computational tasks, such as extensive AI training, large language model processing, or high-performance computing. By leveraging scalable architectures and technologies, these GPUs can work in tandem, providing increased processing power, memory bandwidth, and efficiency necessary for handling the demands of expansive and resource-intensive projects.

Leverage the power of NVIDIA GPUs to excel in a variety of projects. Whether you need single GPU instances for tasks like AI model training and high-performance computing, or scalable GPU Kubernetes clusters for handling complex machine learning models and large datasets, Civo Cloud GPUs offer unparalleled performance and versatility.

For new users, sign up and receive $250 in free credits. Existing users can simply launch their Cloud GPU via the dashboard or CLI.

Civo offers plug-and-play integration and easy configuration, ensuring users familiar with our existing instance services feel right at home. With our setup, it's effortless to scale from startup to hyperscale without any vendor lock-in.