10,000+ research teams trust Lambda
A single GPU system for less than $5500 so you can tackle your AI and ML projects right from your desktop.
Power that doesn't disturbLiquid cooling designs ensure optimal performance without the noise
Next-gen graphics for advanced AI/ML tasksEquipped with the cutting-edge NVIDIA RTX 4090 boasting 24 GB of VRAM
Future-ready architectureState-of-the-art AMD Ryzen 9 7950X CPU, which supports PCIe Gen 5 and DDR5 and offers up to twice the bandwidth of its predecessors
Optimal experience for AI/MLUltimate system configuration with no more guesswork, tailored for AI/ML tasks from fine-tuning Stable Diffusion to handling the complexities of LLAMA 2 7B
Plug in. Start training.
Our desktop PCs 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 requiredAll your favorite frameworks come pre-installed.
Easily upgrade PyTorch® and TensorFlowWhen a new version is released, just run a simple upgrade command.
No more broken GPU driversDrivers will "just work" and keep compatible with popular frameworks.
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 assistanceFrom engineers ready to troubleshoot your hardware, operating system, and machine learning software.
Extra coverAn extended warranty covering hardware failures with rapid repair and replacement.
Always hereProviding you with live help from a dedicated contact via phone or email.
Explore our research
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.
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.
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.
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.
24 GB of GDDR6X, 16,384 CUDA cores, 512 Tensor Cores, PCIe 4.0 x16
- Width: 9.45" (240 mm)
- Height: 19.69" (500 mm)
- Depth: 20.28" (515 mm)
- Weight: 43 pounds (19.5 kg)