Common form factors include compact (SFF), single slot, dual slot, actively cooled, passively cooled, and water cooled. GPU form factor-consider the GPU form factor that matches your node hardware and the number of GPUs you want to run per node.Storage-prefer SSD drives, but SSD might be enough for some scenarios.When computing the total power needed, take into account the CPU, all GPUs running on the node, and other components. Power supply unit-data center grade GPUs are especially power hungry. You will typically use the x16 slots for GPUs and x8 slots for the network card. Ensure you have a GPU board with physically separated PCIe x16 slots and PCIx8 slots. Motherboard-the motherboard should have PCI-express (PCIe) connections for the GPUs you intend to use and for the Infiniband card.You will need to use Infiniband for fast interconnection between GPUs. Networking-each node should have at least two available network ports.RAM-the more system RAM the better, but ensure you have a minimum of 24 GB DDR3 RAM on each node.For most GPU nodes, any modern CPU will do. CPU processor-the node requires a CPU as well as GPUs.When selecting hardware for your node, consider the following parameters: The basic component of a GPU cluster is a node-a physical machine running one or more GPUs, which can be used to run workloads. Use the following steps to build a GPU-accelerated cluster in your on-premises data center. Related content: Read our guide to edge AI This is because each node can generate predictions locally, without having to contact the cloud or a remote data center. Joining GPUs from multiple, distributed nodes into one cluster makes it possible to run AI inference with very low latency. GPU clusters make is possible to ingest large amounts of training data, partition it into manageable units, and train the model in parallel.Įdge AI GPU clusters can also be distributed, with GPU nodes spread across devices deployed at the edge, rather than in a centralized data center. Natural Language Processing (NLP) – large-scale NLP models, such as conversational AI, require a large amount of computational power and continuous training.By using GPU clusters, researchers can accelerate training time and perform fast inference on massive datasets, including video data.
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