# Superclusters for frontier AI

Single-tenant, shared-nothing AI cloud from 4,000 to 165,000+ NVIDIA GPUs, purpose-built and production-ready for large-scale training and inference.

## Do more with every watt

Access more AI compute per watt with liquid-cooled, high-density clusters. Each single-tenant deployment provides full observability and expert co-engineering for maximum performance and production-grade reliability.

[View Superclusters datasheet](https://lambda.ai/hubfs/Datasheet%20-%20Superclusters.pdf)

## Purpose-built AI cloud

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### Enterprise-grade security

Physically isolated, single-tenant clusters with encrypted storage, secure enclosures, and granular access controls

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### Customized for your workloads

Clusters co-engineered to fit your AI workload for a wide range of models, frameworks, and performance requirements

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### High reliability

Pre-handoff burn-in and validation, plus built-in infrastructure redundancy for high availability

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### Observability

Monitoring for power, cooling, networking, storage, and GPU compute using Lambda’s observability stack (Prometheus, OpenTelemetry, and Grafana) or your own system

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### Expert support, 24/7

Dedicated specialists, real-time optimization, and clear escalation paths for mission-critical systems

## High-performance next-gen NVIDIA GPUs

### NVIDIA VR200 NVL72

Rack-scale systems optimized for agentic AI.

- 72× Rubin GPUs / 36× Vera CPUs per rack
- ~75 TB total memory (20.7 TB HBM4 + 54 TB LPDDR5X) / 260 TB/s NVLink bandwidth

### NVIDIA GB300 NVL72

Rack-scale systems optimized for AI reasoning.

- 72× Blackwell Ultra GPUs / 36× Grace CPUs per rack
- 37 TB fast memory / 130 TB/s NVLink Switch bandwidth

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### NVIDIA HGX B300

Peak performance per watt for the largest training runs.

- 72 PF FP8 training / 144 PF FP4 inference
- 2.1 TB HBM3e memory / NVIDIA ConnectX-8 SuperNICs

## Maximum network fabric performance

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### NVLink domain

Ultra-fast GPU-to-GPU within node/rack. Low latency, high throughput, and no PCIe bottlenecks for model-parallel training and collectives.

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### Non-blocking InfiniBand

Lossless, low-latency fabric with RDMA and adaptive routing. SHARP for predictable, large-scale distributed training.

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### RoCE (RDMA over Converged Ethernet)

RDMA performance on Ethernet with kernel bypass, PFC/ECN, and spine–superspine designs to extend AI networking across hybrid and multi-cloud with InfiniBand-class behavior.

## Storage optimized for cost and speed

Our tiered storage architecture integrates HBM, DDR, NVMe, and data lakes to deliver high throughput and low latency for AI workloads.

## Fully managed AI infrastructure

Lambda’s managed services deliver production-grade orchestration and cluster ops for AI and HPC.

### Managed Kubernetes

Bare-metal performance with proximity-aware scheduling, auto node recovery, and continuous monitoring. We manage the control plane and hardware; you run container-native workloads at scale.

[Learn more](https://lambda.ai/kubernetes)

### Managed Slurm

SLA-backed job scheduling for large AI and HPC clusters—control plane deployment, updates, monitoring, and efficient GPU utilization without the operational overhead.

[Learn more](https://lambda.ai/slurm)

## Build with the best

Lambda AI factories are engineered in partnership with NVIDIA, Supermicro, and Dell Technologies.

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## Superclusters for frontier AI

Single-tenant, shared-nothing AI cloud from 4,000 to 165,000+ NVIDIA GPUs, purpose-built and production-ready for large-scale training and inference.