Lambda has launched a new Hyperplane server combining the fastest GPU on the market, NVIDIA H100, with the world’s best data center CPU, AMD EPYC 9004.
This post discusses the Total Cost of Ownership (TCO) for a variety of Lambda A100 servers and clusters. We calculate the TCO for individual Hyperplane-A100 servers, compare the cost with renting a AWS p4d.24xlarge instance, and walk through the cost of building and operating A100 clusters.
Introducing the Lambda Echelon, a GPU cluster designed for AI. It comes with the compute, storage, network, power, and support you need to tackle large scale deep learning tasks. Echelon offers a turn-key solution to faster training, faster hyperparameter search, and faster inference.
This post uses our Total Cost of Ownership (TCO) calculator to examine the cost of a variety of Lambda Hyperplane-16 clusters. We have the option to include 100 Gb/s EDR InfiniBand networking, storage servers, and complete rack-stack-label-cable service.
Scaling out deep learning infrastructure becomes easier with 16 NVIDIA Tesla V100 GPUs and preinstalled frameworks like TensorFlow, Keras, and PyTorch.
A cost and speed comparison between the Lambda Hyperplane 8 V100 GPU Server and AWS p3 GPU instances. A very similar comparison to the DGX-1.
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