Lambda Inference

Inference Server for Deep Learning

12-GPU / 16-GPU / 20-GPU Options

Tesla T4 Server for Deep Learning Inference

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

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

12x Tesla T4 GPUs

2x Xeon Silver 4116 (12 Cores, 2.10 GHz)
12x NVIDIA T4 (16 GB)
256 GB RAM
Fully Customizable Storage
10 Gbps Ethernet
Lambda Support Contract - 1 year
$ 46,292
Customize
Premium

16x Tesla T4 GPUs

2x Xeon Gold 6148 (20 cores, 2.40 GHz)
16x NVIDIA T4 (16 GB)
512 GB RAM
Fully Customizable Storage
100 Gbps InfiniBand
Lambda Support Contract - 1 year
$ 66,580
Customize
Max

20x Tesla T4 GPUs

2x Xeon Platinum 8168 (24 Cores, 2.70 GHz)
20x NVIDIA T4 (16 GB)
512 GB RAM
Fully Customizable Storage
100 Gbps InfiniBand
Lambda Support Contract - 3 years
$ 85,372
Customize
custom

+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.

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