Lambda Hyperplane-16

GPU Server with 16 Tesla V100s

Up to sixteen Tesla V100 GPUs with NVLink. Save up to 90% by moving your Deep Learning workload from cloud to on-premise.

Hyperplane-16, GPU Servers with 16x Tesla V100s

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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Top Configurations

Optimized configurations that won't bottleneck

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

Basic
Hyperplane Basic, a GPU Server with 4x Tesla V100

16x Tesla V100 Server

16-Way NVLink
2x Xeon Platinum 8268 (24 Cores)
768 GB of System Memory
1.92 TB NVMe OS Drive
Customizable Data Drives
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$ 223,600
Customize
Premium
Hyperplane Premium, a GPU Server with 8x Tesla V100

16x Tesla V100 Server

16-Way NVLink
2x Xeon Platinum 8268 (24 Cores)
1.5 TB of System Memory
1.92 TB NVMe OS Drive
Customizable Data Drives
Dual-Port 100 Gb/sec Ethernet/IB for Storage
8x 100 Gb/sec IB for Multi-Node Training
$ 241,392
Customize
Max
Hyperplane Max, a GPU Server with 8x Tesla V100

16x Tesla V100 Server

16-Way NVLink
2x Xeon Platinum 8280M (28 Cores)
3 TB of System Memory
1.92 TB NVMe OS Drive
Customizable Data Drives
Dual-Port 100 Gb/sec Ethernet/IB for Storage
8x 100 Gb/sec IB for Multi-Node Training
$ 276,360
Customize
custom

4x or 8x Tesla V100

NVLink
Any Processor
Up to 768 GB RAM
Fully Customizable Storage
100 Gbps InfiniBand
+1 (866) 711-2025
Live Chat

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.

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