Lambda Blade

Deep Learning Server with up to 10 GPUs

GPU server with up to ten customizable GPUs. Pre-installed with Ubuntu, TensorFlow, PyTorch, Keras, CUDA, and cuDNN.

8x GPU Server for Deep Learning

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.

Apple is a customer MIT is a customer Los Alamos National Lab is a customer Carnegie Mellon University is a customer Google is a customer Wells Fargo is a customer Anthem is a customer

Recommended Designs

Optimized configurations that won't bottleneck

Our recommended designs are benchmarked and tuned to eliminate CPU, memory, and storage bottlenecks when running deep learning workloads.

Custom
Lambda Blade Basic, a Deep Learning Server with up to 10x GPUs

Up to 10x GPUs

In Stock
2x CPUs (Up to 56 Total Cores)
Up to 10x GPUs
Up to 6 TB of Memory
Storage is Customizable
Networking is Customizable
+1 (866) 711-2025
Live Chat
Premium
Lambda Blade Premium, a Deep Learning Server with 8x RTX 6000

8x Quadro RTX 6000

In Stock
2x Xeon Gold 5218 (16 Cores)
8x RTX 6000 GPUs
512 GB of Memory
1.92 TB NVMe SSD
4 TB SATA SSD
$ 42,138
Customize
Max
Lambda Blade Max, a Deep Learning Server with 8x RTX 8000

8x Quadro RTX 8000

In Stock
2x Xeon Gold 5218 (16 Cores)
8x RTX 8000 GPUs
768 GB of Memory
3.84 TB NVMe SSD
4 TB SATA SSD
$ 59,273
Customize
Custom

In Stock
+1 (866) 711-2025
Live Chat

Fully Customizable

Not seeing what you're looking for?

GPUs, processors, storage, networking, and memory are fully customizable. Get in touch and we'll design a system that matches your specifications.

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