GPU Solutions for Deep Learning

Deep Learning Workstations, Servers, Laptops, and Cloud

GPU-accelerated with TensorFlow, PyTorch, Keras, and more pre-installed. Just plug in and start training. Save up to 90% by moving off your current cloud and choosing Lambda.

Trusted by thousands of customers worldwide

Researchers and engineers at universities, start-ups, Fortune 500s, public agencies, and national labs use Lambda to power their artificial intelligence workloads.

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TensorBook
GPU Laptop

Deep Learning Laptop with RTX 2070 Max-Q or RTX 2080 Max-Q. Up to 32 GB Memory.

Lambda Dual
2x GPU Workstation

Deep Learning Workstation with RTX 2080 Ti, Titan RTX, RTX 8000, or Titan V GPUs. NVLink Available.

Lambda Quad
4x GPU Workstation

Deep Learning Workstation with RTX 2080 Ti, RTX 6000, RTX 8000, or Titan V GPUs. NVLink Available.

Lambda Blade
8x GPU Server

Deep Learning Server with up to 10x GPUs and NVLink. Quadro RTX 8000, RTX 6000, RTX 5000 available.

Lambda Hyperplane
Tesla V100 Server

Tesla V100 Server with NVLink for Deep Learning. Up to 8 GPUs, 768 GB of Memory. Fully Customizable

Zero setup, easy updates

Lambda Stack comes free with your computer. Machine Learning libraries work out-of-the box and can be updated automatically.

Ubuntu 18.04 comes pre-installed
TensorFlow comes pre-installed
PyTorch comes pre-installed
Keras comes pre-installed

Explore Lambda's Research

Our 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