Special Deals
Free Trials
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AI Accelerated!
starting at $2.5/hour
What We Offer
Pre-Configured Flavours
Enhance the efficiency of any GPU-accelerated task using preconfigured options or tailor custom configurations to meet your specific requirements.
Top-Notch API
Top-notch API (CLI, GraphQL) to streamline your workflow and instantly provision GPUs.
Quick Deployment
Deploy GPU instances based on containers, launching within seconds, using both public and private repositories.
Managed Kubernetes
Managed Kubernetes containers deliver high performance without infrastructure hassles. Enjoy rapid instance provisioning and responsive auto-scaling across thousands of GPUs.
Enterprise Grade
Enterprises can access the AI frameworks and tools necessary for building GPU-accelerated workflows, including AI chatbots, recommendation engines, vision AI, and more.
Premium Storage
Fault-tolerant cloud storage with triple replication. You can easily adjust volumes for optimized IOPS and superior performance.
Optimised Networking
Easily scale your network for HPC workloads with routing, switching, firewalling, and load-balancing, all without egress charges.
Zolute - One stop solution for your AI journey
From Infrastructure to Platform that serve your Enterprise AI needs.
Bring Enterprises closer to AI mainstream workload with uncompromising
performance of NVIDIA power house.
At Zolute, we are dedicated to supporting enterprises in meeting
their AI ambitions and timelines, providing top-notch support every
step of the way. Join us as we elevate your AI experience and propel
your business into the future.
Why Zolute ?
Digital Footprint accross Asia
Top-Tier security
Flexible pricing
24/7 Support Center
High Performance Storage
Digital Footprint accross Asia
Products
| Architecture: | NVIDIA Hopper |
| CUDA Cores: | Up to 16,896 |
| Memory: | 80 - 188 GB HBM2 |
| Tensor Cores: | Yes |
| Memory Bandwidth: | 3.35 - 7.8 TB/s |
| Form Factor: | SXM, PCle, 2xPCle |
| Architecture: |
NVIDIA Grace Hopper Superchip |
| CUDA Cores: | N/A |
| Memory: | 282GB HBM3e |
| Tensor Cores: | Yes |
| Memory Bandwidth: | Up to 2 TB/s |
| Form Factor: | PCle |
| Architecture: | NVIDIA Ada Lovelace |
| CUDA Cores: | Up to 6,912 |
| Memory: | 48GB GDDR6 with ECC |
| Tensor Cores: | Yes |
| Memory Bandwidth: | Up to 864GB/s |
| Form Factor: | 4.4" (H) x 10.5" (L), dual slot |
| Architecture: | NVIDIA Hopper |
| CUDA Cores: | Up to 10,752 |
| Memory: | 48 GB GDDR6 |
| Tensor Cores: | Yes |
| Memory Bandwidth: | Up to 696 GB/S |
| Form Factor: | 4.4" (H) x 10.5" (L) Dual Slot |
Zolute
In association with GreenNode
Zolute is in strategic partnership with Green Node, a Nvidia preferred partner. Zolute solidifies the position as a trusted and forward-thinking cloud provider in the AI/ML ecosystem.
FAQ
We are scheduled to launch the service by January 2024. You can secure your GPUs today by clicking "Reserve now" at the top of the page.
The NVIDIA H100 GPU introduces several key innovations:
- Fourth-generation Tensor Cores: These cores excel at matrix computations, significantly enhancing the efficiency of a wide range of AI and HPC workloads.
- Transformer Engine: The H100 GPU incorporates a new Transformer Engine, delivering remarkable speed improvements. It can achieve up to 9x faster AI training and up to 30x faster AI inference compared to the prior-generation A100 GPU, particularly benefiting large language models.
- NVLink Network Interconnect: The GPU features a new NVLink Network interconnect, enabling seamless GPU-to-GPU communication. It can connect up to 256 GPUs across multiple compute nodes, facilitating efficient data exchange and parallel processing.
- Secure MIG (Multi-Instance GPU): Secure MIG partitions the GPU into isolated instances, optimizing Quality of Service (QoS) for smaller workloads. This ensures that different tasks running on the GPU do not interfere with each other, enhancing overall performance and security.
Our GPUs can empower your AI/ML models by significantly increasing their processing speed, enabling the handling of large and complex models, and offering the efficiency needed for real-time applications. This leads to more accurate results and quicker insights, ultimately enhancing the performance and capabilities of your AI/ML projects.
Deploying an AI/ML model on the cloud can be a complex process, but with careful preparation, you can ensure a smooth deployment.
Here are the 10 key considerations to help you prepare for deploying an AI/ML model on the cloud:
- Data Preparation: Ensure your dataset is well-prepared, cleaned, and ready for use. Data quality is crucial for model accuracy.
- Model Deployment: Develop and train your AI/ML model using appropriate frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Optimize the model for production use.
- Scalability and Resource Planning: Determine the computational resources you need. We offer a variety of GPU options. Consider your scalability requirements as well.
- Security: Implement robust security measures, including encryption, access control, and authentication, to protect your data and model.
- Containerization: Containerize your model using tools like Docker. Containers ensure consistency across different environments.
- Microservices Architecture: Consider breaking down your application into microservices for better scalability and maintainability.
- Infrastructure as Code (IaC): Use IaC tools like Terraform to define your cloud infrastructure, ensuring consistency and easy management.
- Monitoring and Logging: Implement monitoring and logging solutions to track model performance, resource utilization, and system health.
- CI/CD: Create a CI/CD pipeline to automate model deployment, testing, and updates.
- Cost Management: Monitor and optimize costs by using our vConsole tools and Pay-as-you-go pricing.
