Lambda secured a $44 million Series B to accelerate the growth of our AI cloud. Funds will be used to deploy new H100 GPU capacity with high-speed network interconnects and develop features that will make Lambda the best cloud in the world for training AI.
Lambda is hiring! Join a fast growing startup providing deep learning hardware, software, and cloud services to the world's leading companies.
If you're trying to figure out how to build and scale your team's deep learning infrastructure, this presentation is for you. We walk you through the decisions associated with building cloud, on-prem, and hybrid infrastructure for your team. We've distilled best practices learned from helping thousands of teams build their
Deep learning is the most important technology to impact gaming since the advent of 3D graphics. This short video presentation walks you through a few of the technologies that will deliver unbelievable gaming experiences in the near future. Research covered in this presentation: 1. Photorealistic neural rendering 2. Deepfakes for
This curriculum provides an overview of free online resources for learning about deep learning. It includes courses, books, and even important people to follow. If you only want to do one thing, do this: Train an MNIST network with PyTorch. https://github.com/pytorch/examples/tree/master/mnist Introductory CS231n:
Full Video TutorialThis tutorial shows you how to install Docker with GPU support on Ubuntu Linux. To get GPU passthrough to work, you'll need docker, nvidia-container-toolkit, Lambda Stack, and a docker image with a GPU accelerated library. 1) Install Lambda Stack LAMBDA_REPO=$(mktemp) && \ wget -O${LAMBDA_REPO} https://lambdalabs.
Lambda secured $24.5M in financing, including a $15M Series A equity round and a $9.5M debt facility that will allow for the growth of Lambda GPU Cloud and the expansion of Lambda's on-prem AI infrastructure software products. Read more details in the post.
Introducing the Lambda EchelonLambda Echelon [https://lambdalabs.com/gpu-cluster/echelon] is 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.
Benchmarks for ResNet-152, Inception v3, Inception v4, VGG-16, AlexNet, SSD300, and ResNet-50 using the NVIDIA A100 GPU and DGX A100 server.
In this post we'll walk through using 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. The purpose of this post is to
This tutorial will walk you through the steps required to set up a Mellanox SB7800 36-port switch. The subnet manager discovers and configures the devices running on the InfiniBand fabric. This tutorial will show you how to set it up via the command line or via the web browser.
This presentation is a high-level overview of the different types of training regimes that you'll encounter as you move from single GPU to multi GPU to multi node distributed training. It briefly describes where the computation happens, how the gradients are communicated, and how the models are updated and communicated.
RTX 2080 Ti vs. RTX 2080 vs. Titan RTX vs. Tesla V100 vs. Titan V vs. GTX 1080 Ti vs. Titan Xp benchmarks neural net training.
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