New! Cloud GPU servers from $1.25/hour

GPU workstation built for Deep Learning

NVIDIA RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, and Quadro RTX 6000 options. Pre-installed with Ubuntu, TensorFlow, PyTorch, CUDA, and cuDNN.

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5000+ research groups trust Lambda

Easy system administration

Our deep learning workstation comes with Lambda Stack, which includes frameworks like TensorFlow and PyTorch. Lambda Stack makes upgrading these frameworks easy.

Ubuntu 20.04
TensorFlow
PyTorch
Keras
NVIDIA CUDA
NVIDIA cuDNN

Technical specifications

GPU
Up to 4x NVIDIA GPUs
RTX 30XX, RTX A6000, RTX A5000, RTX A4000, and Quadro RTX options
Processor
AMD Threadripper or Intel Core i9
Configurable up to 64 cores, 128 threads, and 256 MB cache
Memory
Up to 1 TB
Fits up to eight 128 GB LRDIMMs at 3200 MHz
OS drive
Up to 2 TB
3,200 MB/s seq. read and 2,000 MB/s seq. write
Extra storage
Up to 61 TB
Fits up to eight 7.68 TB SATA SSDs.
Power supply
Up to 1600 watts
of maximum continuous power at voltages between 100 and 240V
Size & weight
Width: 13.1" (332 mm)
Height: 16.3" (415 mm)
Depth: 18.4" (458 mm)
Weight: 38 pounds (17.2 kg)

Explore our research

Lambda's research papers have been accepted into the top machine learning and graphics conferences, including ICCV, SIGGRAPH Asia, NeurIPS, and ACM Transactions on Graphics (TOG).

HoloGAN: Unsupervised Learning of 3D Representations from Natural Images
We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Learn More
RenderNet: A Deep Conv. Network for Differentiable Rendering from 3D Shapes
Traditional computer graphics rendering pipelines are designed for procedurally generating 2D images from 3D shapes with high performance. Learn More
Adversarial Monte Carlo Denoising with Conditioned Aux. Feature Modulation
Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Learn More