Accelerate AI model inference for applications like conversational AI and natural language processing (NLP). The A30 is suitable for small to medium-scale deep learning training, fine-tuning, and transfer learning, offering a practical option for budget-conscious projects.
Support your scientific and engineering simulations such as energy modeling and computational fluid dynamics (CFD). The A30 provides dependable performance for traditional HPC tasks, making it a viable choice for established workflows.
Efficiently process and analyze large datasets. The A30 performs well in GPU-accelerated ETL operations, real-time analytics, and machine learning tasks, offering a balanced approach to data processing.
Our Tesla A30 GPU is powered by the NVIDIA A30 accelerator, built on the Ampere architecture to deliver balanced performance for AI inference, training, data analytics, and HPC workloads. With 24 GB of HBM2 memory and support for Multi-Instance GPU (MIG), it enables efficient resource utilization across a wide range of use cases.
This GPU is optimized for real-world applications such as NLP, computer vision, ETL operations, and scientific computing. Ideal for teams seeking scalable AI performance with low power consumption and strong cost-efficiency.
GPU A30 enables data scientists and researchers to accelerate tasks like energy modeling, genomics, and computational fluid dynamics (CFD) simulations. Major frameworks such as TensorFlow and PyTorch run efficiently, making it a versatile choice for production and research environments.
This GPU is optimized for AI workloads like natural language processing, conversational AI, computer vision tasks, and recommendation systems. It delivers consistent performance for small to medium-scale training, fine-tuning, and transfer learning while maintaining efficient resource usage.
| RAM | CPU Cores | GPU Cards | Min Local Storage | Max Local Storage | Price / Hour ({{ currency | uppercase }}) | |
|---|---|---|---|---|---|---|
| Small | 56 GB | 12 Cores | 1 GPU | 100 GB | 800 GB | {{ prices.opencompute.gpua30.small[currency] | number:8 }} |
| Medium | 90 GB | 16 Cores | 2 GPU | 100 GB | 1.2 TB | {{ prices.opencompute.gpua30.medium[currency] | number:8 }} |
| Large | 120 GB | 24 Cores | 3 GPU | 100 GB | 1.6 TB | {{ prices.opencompute.gpua30.large[currency] | number:8 }} |
| Huge | 225 GB | 48 Cores | 4 GPU | 100 GB | 1.6 TB | {{ prices.opencompute.gpua30.huge[currency] | number:8 }} |
Local Storage is not included in the displayed Instances price, and has a cost of {{ prices.opencompute.volume[currency] | number:8 }} {{ currency | uppercase }} / GiB hour.
Instance needs to be shutdown for hypervisor and platform updates as it cannot be live-migrated.
Available in the following Zones: CH-GVA-2
Please note that GPU instances require account validation: access is provided with priority to established businesses, and is granted after a manual screening process.
Our GPU A30 comes at a fairly priced cost to performance ratio. Choose between four different options, from 1 to 4 GPUs coming with SSD storage from 100 GB up to 1.6 TB depending on the chosen instance type.
Combine the simplicity and scalability of the Exoscale Cloud with the power of NVIDIA GPUs. With our Docker based template you can access the full potential of the NVIDIA GPU Cloud (NGC) and significantly reduce time to solution.
NVIDIA GPU Cloud (NGC) provides a selected set of GPU-optimized software for artificial intelligence applications,
visualizations and HPC. The NGC Catalog includes containers, pre-trained models, Helm charts for Kubernetes deployments
and specific AI toolkits with SDKs.
NGC catalog works with both Exoscale Compute and Exoscale SKS. The image template for SKS worker nodes embeds the optimized version
of the container runtime for Nvidia cards making it quick work of starting any application from the catalog without
fiddling with drivers and CUDA versions.
24 GB high-bandwidth memory (HBM2) ensures rapid data transfers, enabling smooth execution of AI workloads, data analytics, and HPC applications.
Each A30 GPU card is provided with dedicated resources, ensuring consistent performance for your applications without resource contention.
Benefit from NVIDIA's Ampere architecture, offering strong compute capabilities and efficiency for its class.
The NVIDIA A30 is optimized for High-Performance Computing (HPC), AI inference, deep learning training, and data analytics, making it a versatile choice for many projects.
Partition your Tesla A30 GPU into up to 4 separate, isolated instances to efficiently manage multiple applications simultaneously, enhancing resource utilization.
As a sovereign European Cloud Provider, Exoscale ensures all your data is stored in the country of your chosen zone, fully GDPR compliant.
| Description | Specifications |
|---|---|
| Graphic Card | NVIDIA A30 |
| Cuda Cores per card | 10752 |
| Tensor Cores per card | 336 |
| GPU memory per card | 24 GB |
| GPU cards | 1-4 |
| CPU cores | 12-48 |
| RAM per instance | 56-225 |
| SSD local storage | max. 1.6 TB (SATA) |
| Zone | CH-GVA-2 |
| Works with | Compute SKS Docker NVIDIA |
When running mission critical production workloads in the cloud, a partner you can rely on makes all the difference. Our customer success engineers have helped hundreds of customers from all over Europe migrate, run and scale production workloads on Exoscale.
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