GPU server with 4x Tesla V100s

1000+ research groups trust Lambda

Easy system administration

Our GPU servers comes with Lambda Stack, which includes frameworks like TensorFlow, PyTorch, and Keras. Lambda Stack makes upgrading these frameworks easy.

Ubuntu 20.04
Keras
TensorFlow
PyTorch
Caffe
Theano
NVIDIA CUDA
NVIDIA cuDNN

Technical specifications

OS
Ubuntu 20.04
Configurable to Ubuntu 18.04 and Windows 2019 Server
GPU
4x NVIDIA Tesla V100s
each with 32 GB of VRAM and fully connected with NVLink
Processors
2x Intel Xeon Scalable processors
each with up to 20 cores, 40 threads, and 27.5 MB cache
Memory
Up to 3 TB
of 2933 MHz DDR4 ECC memory in twelve LRDIMM slots
OS drive
Up to 3.84 TB
NVMe SSD
Storage
Up to 82 TB
of NVMe SSDs
Networking
2x 10 Gb/s ethernet ports (RJ45) with support for 1 Gb/s and 100 Mb/s
1x 1 Gb/s ethernet port (RJ45) for IPMI with support for 100 Mb/s
I/O ports
2x USB 3.0 ports
1x VGA connector (your monitor must support VGA, adapters will not work)
Power
Power supplies: 2x 2000W redundant Titanium-rated PSUs
Input power requirements: 100-240V, 50-60 Hz
Max power draw: 1750W (16 amps at 110V, 7 amps at 240V)
Size & weight
Width: 17.2" (437 mm)
Height: 1.7" (43 mm)
Depth: 35.2"(894 mm)
Weight: 45 lbs (20.41 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