Gcore GPU Cloud infrastructure services provide high-performance compute clusters designed for machine learning tasks.
You can train your ML models with the latest NVIDIA GPUs. We offer a wide range of Bare Metal servers and Virtual Machines powered by NVIDIA A100, H100, and L40S GPUs.
You can choose between multiple configurations and reservation plans that would best fit your computing requirements.
Specification | Characteristics | Use case | Performance |
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H100 with Infiniband |
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Ultimate performance for compute-intensive tasks that require a significant exchange of data by the network. |
A100 with Infiniband |
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Well-balanced in performance and price. |
A100 without Infiniband |
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The best solution for inference models that require more than 48GB vRAM. |
L40 |
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The best solution for inference models that require less than 48GB vRAM. |
Check out the pricing at our official website: AI GPU Cloud infrastructure.
Our Graphcore infrastructure consists of three entities:
Poplar server manages all the other servers in the cluster. You have full access to this server via SSH and can work with it directly to manage the infrastructure and run your model.
M2000 or Bow-2000 server (different types are available in different regions) is used for calculations made while training your model. You don’t have access to it, and this server receives commands from the Poplar server.
vIPU controller (virtual Intelligence Processing Unit) is a service which configures M2000/Bow-2000 servers of your AI infrastructure to form a cluster. It's involved while the cluster is being created and while you’re changing its configuration (e.g. resizing partitions). You have access to vIPU controller via API and can rebuild the cluster if needed.
For datasets storage, you can use Poplar server disk space, external S3 storage, or Gcore Object Storage.
We provide two types of Graphcore servers: M2000 and Bow-2000. M2000 is a second-generation machine and Bow-2000 is a third-generation one.
Specification | Characteristics |
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IPU processors |
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AI compute |
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Memory |
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Streaming Memory |
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IPU-Gateway |
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Internal SSD |
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Mechanical |
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Lights-outmanagement |
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Specification | Characteristics |
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IPU processors |
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AI compute |
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IPU-Fabric |
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IPU-Gateway |
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Streaming Memory |
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Internal SSD |
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Mechanical |
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Lights-out management |
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Tool class | List of tools | Explanation |
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Framework | TensorFlow, Keras, PyTorch, Paddle Paddle, ONNX, Hugging Face | Your model is supposed to use one of these frameworks for correct work. |
Data platforms | PostgreSQL, Hadoop, Spark, Vertika | You can set up a connection between our cluster and your data platforms of these types to make them work together. |
Programming languages | JavaScript, R, Swift, Python | Your model is supposed to be written in one of these languages for correct work. |
Resources for receiving and processing data | Storm, Spark, Kafka, PySpark, MS SQL, Oracle, MongoDB | You can set up a connection between our cluster and your resources of these types to make them work together. |
Exploration and visualization tools | Seaborn, Matplotlib, TensorBoard | You can connect our cluster to these tools to visualize your model. |
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