GPU Workstation for Deep learning

Up to four fully customizable NVIDIA GPUs. Pre-installed with Ubuntu, TensorFlow, PyTorch®, NVIDIA CUDA, and NVIDIA cuDNN.

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Plug in. Start training.

Our servers include Lambda Stack, which manages frameworks like PyTorch® and TensorFlow. With Lambda Stack, you can stop worrying about broken GPU drivers and focus on your research.

Zero configuration required
All your favorite frameworks come pre-installed.
Easily upgrade PyTorch® and TensorFlow
When a new version is released, just run a simple upgrade command.
No more broken GPU drivers
Drivers will "just work" and keep compatible with popular frameworks.

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Lambda System Support

With our System Support option, you can focus on your code and leave the rest to us. If something goes wrong with your machine, whether it be a driver issue, kernel panic, or hardware failure, our support team can debug it.

Expert assistance
From engineers ready to troubleshoot your hardware, operating system, and machine learning software.
Extra cover
An extended warranty covering hardware failures with rapid repair and replacement.
Always here
Providing you with live help from a dedicated contact via phone or email.

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Explore our research

ICCV 2021

Multiple Pairwise Ranking Networks for Personalized Video Summarization

We propose a model for personalized video summaries by conditioning the summarization process with predefined categorical labels.

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ICCV 2019

HoloGAN: Unsupervised Learning of 3D Reps. from Natural Images

We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images.

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NeurIPS 2018

RenderNet: A Deep ConvNet for Differentiable Rendering from 3D Shapes

We present a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes.

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SIGGRAPH Asia 2019

Adversarial Monte Carlo Denoising with Conditioned Aux. Feature Modulation

We demonstrate that GANs can help denoiser networks produce more realistic high-frequency details and global illumination.

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View all research

Technical Specifications

GPUs

Up to 4 dual-slot PCIe GPUs. Options include:

NVIDIA RTX PRO 6000 Blackwell Workstation Edition: 96 GB of GDDR7, 24,064 CUDA Cores, 752 Tensor Cores, PCIe 5.0x16, 600W TDP
NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: 96 GB of GDDR7, 24,064 CUDA Cores, 752 Tensor Cores, PCIe 5.0x16, 300W TDP
NVIDIA RTX PRO 5000 Blackwell: 48 GB of GDDR7, 12,800 CUDA Cores, 400 Tensor Cores, PCIe 5.0x16, 300W TDP
NVIDIA RTX PRO 4500 Blackwell: 32 GB of GDDR7, 10,496 CUDA Cores, 328 Tensor Cores, PCIe 5.0x16, 200W TDP
NVIDIA RTX PRO 4000 Blackwell: 24 GB of GDDR7, 8,960 CUDA Cores, 280 Tensor Cores, PCIe 5.0x16, 140W TDP

Processor

AMD Ryzen Threadripper PRO 7975WX 32-Core, 64-Thread...

AMD Ryzen Threadripper PRO 7975WX 32-Core, 64-Thread
AMD Ryzen Threadripper PRO 7985WX 64-Core, 128-Thread
AMD Ryzen Threadripper PRO 7995WX 96-Core, 192-Thread

Memory

Up to eight 128 GB RDIMMs at 4800 MHz

OS Drive

Up to 7,400 MB/s seq. read and 7,000 MB/s seq. write

Extra Storage

Compatible with up to three 3.84 TB M.2 NVMe SSDs and two 30.72 TB U.2 SSDs

Power Supply

Up to 1600 watts of maximum continuous power at voltages between 100 and 240V

Size & weight

Width: 9.5" (240mm)
Height: 22.8" (580mm)
Depth: 22.0" (560mm)
Weight: 43-55 lbs

Vector Pro datasheet

Download the Vector Pro Datasheet