Lambda
PaidThe Superintelligence Cloud for training and inference at scale.
Lambda is a GPU cloud infrastructure platform built specifically for AI workloads, offering on-demand instances, pre-configured clusters, and dedicated superclusters powered by NVIDIA GPUs. It provides a complete stack that combines high-density power, liquid cooling, and high-bandwidth networking into AI factories designed for training foundation models and serving inference at scale. ML engineers, researchers, and enterprise AI teams use Lambda to spin up GPU resources in minutes and scale from a single GPU to hundreds of thousands without building their own data centers.
What is it
A GPU cloud platform that delivers AI factories integrating NVIDIA GPUs, high-density power, and liquid cooling for peak AI performance.
What it can do
Launch on-demand GPU instances, deploy production-ready 1-Click Clusters with InfiniBand, and run single-tenant superclusters, all preloaded with Lambda Stack and managed orchestration.
Who is it for
ML engineers, AI researchers, foundation model teams, and enterprises that need dedicated, high-performance compute for training and inference.
Key Features
On-Demand GPU Instances
Spin up 1 to 8 GPU instances in minutes through a self-serve dashboard, API, or CLI. Instances ship with NVIDIA HGX B200, H100, A100, or GH200 GPUs and include local NVMe storage, so users can start training or inference without hardware procurement delays.
1-Click Clusters
Deploy production-ready clusters of 16 to 2,000+ NVIDIA HGX B200 or H100 GPUs connected by NVIDIA Quantum-2 InfiniBand. Clusters come fully optimized for distributed AI workloads with managed Kubernetes or Slurm orchestration and S3-compatible storage.
Superclusters
Lease single-tenant, shared-nothing AI factories ranging from 4,000 to 165,000+ NVIDIA GPUs. These deployments include liquid cooling, NVLink domains, non-blocking InfiniBand, and physical isolation for mission-critical workloads.
Lambda Stack
A curated software environment preinstalled on every instance and cluster that includes NVIDIA drivers, CUDA, cuDNN, NCCL, PyTorch, TensorFlow, JAX, Docker, and JupyterLab. It is tested for compatibility across Lambda systems and can be kept current with standard package updates.
Use Cases
Foundation model training
Research labs and AI companies distribute training runs across hundreds or thousands of InfiniBand-connected GPUs to reduce time-to-model for large language and multimodal models.
Fine-tuning production models
Teams take open-weight models and adapt them on domain-specific data using 8-GPU HGX instances or small 1-Click Clusters without building on-prem clusters.
Large-scale inference serving
Engineering teams deploy models that must serve billions of tokens with predictable latency by leveraging high-memory GPUs and low-latency RDMA networking.
AI research prototyping
Individual researchers and startups launch single-GPU instances in minutes to experiment with architectures, run ablation studies, and validate ideas before scaling up.
Pricing plans
Frequently Asked Questions
Discussion
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