Lambda 1-Click Clusters
Lambda 1-Click Clusters is a managed, production-ready GPU cluster service providing instant access to pre-configured multi-node NVIDIA B200 and H100 GPU clusters scaling from 16 to 2,000+ GPUs with full InfiniBand interconnects and zero management overhead. Unlike individual GPU instances or expensive single-tenant contracts, 1-Click Clusters are purpose-built for large-scale distributed AI model training, offering flexibility through on-demand or reserved pricing and instant provisioning in minutes.
Lambda 1-Click Clusters operates as a managed multi-node GPU cluster service combining NVIDIA HGX B200 or H100 systems connected via NVIDIA Quantum-2 InfiniBand non-blocking fabric, pre-installed software stacks (PyTorch, TensorFlow, CUDA via Lambda Stack), and automatic orchestration (Kubernetes or Slurm). Customers provision fully configured clusters within minutes with 3 dedicated CPU management nodes per cluster for scheduling, enabling direct SSH access without manual infrastructure setup or configuration.
Key Features
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Instant multi-node provisioning (16-2,000+ GPUs): Launch fully configured clusters in minutes without infrastructure procurement delays.
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Full InfiniBand interconnect (non-blocking fabric): Quantum-2 InfiniBand with lossless transmission eliminates inter-node communication bottlenecks typical of Ethernet-based cloud clusters.
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Pre-installed Lambda Stack (PyTorch, TensorFlow, CUDA, cuDNN): All ML frameworks and NVIDIA libraries pre-installed reduce provisioning time from hours to minutes.
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Flexible orchestration (Kubernetes or Slurm): Choose container-native or HPC batch scheduling—Lambda manages both without customer overhead.
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Dedicated management nodes per cluster: 3 CPU nodes per cluster with static public IP and SSH access for direct management without Lambda intermediation.
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On-demand or reserved capacity pricing: Pay-by-the-minute on-demand or reserve for 1-3 years at 20-40% discounts for cost optimization.
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No egress fees and transparent billing: Billed per GPU-hour with no hidden data transfer costs—enabling accurate budget forecasting.
Ideal For & Use Cases
Target Audience: AI research teams and enterprises requiring multi-node GPU clusters with infrastructure flexibility, organizations training large models (10B+ parameters), and development teams needing rapid iteration on cluster configurations.
Primary Use Cases:
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Large-scale model training and fine-tuning: Research teams and enterprises train 10B-100B+ parameter models using distributed frameworks (DeepSpeed, FSDP, Megatron-LM) with fast gradient synchronization via InfiniBand.
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Short-term research experiments: Academic labs and startups prototype architectures and validate hypotheses using temporary cluster capacity without long-term commitments.
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Enterprise model fine-tuning: Organizations fine-tune foundation models on proprietary datasets, release resources after training—optimizing costs for episodic, high-intensity workloads.
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Parallel hyperparameter sweeps: ML teams run multiple training runs across different configurations simultaneously, accelerating model selection cycles.
Deployment & Technical Specs
| Category | Specification |
|---|---|
| Architecture/Platform Type | Managed multi-node GPU cluster with dedicated management nodes; NVIDIA Quantum-2 InfiniBand non-blocking fabric; fully provisioned and orchestrated |
| GPU Variants | NVIDIA HGX B200 (8× B200 per system), NVIDIA H100 SXM (8× H100 per node); 16 to 2,000+ GPU clusters |
| Cluster Scaling | Minimum 16 GPUs, maximum 2,000+ GPUs |
| Network Fabric | NVIDIA Quantum-2 InfiniBand (non-blocking, lossless, SHARP-capable) within cluster |
| Management Nodes | 3 dedicated CPU nodes per cluster for Kubernetes/Slurm control; static public IP and SSH access |
| Orchestration Options | Managed Kubernetes (container orchestration) or Managed Slurm (HPC batch scheduling) |
| Pre-installed Software | Lambda Stack: PyTorch, TensorFlow, CUDA 12.x, cuDNN, NCCL, Apex, DeepSpeed, Megatron-LM |
| Storage per Node | 22 TiB SSD per 8-GPU node |
| Provisioning Speed | Clusters provisioned within minutes; no procurement or manual setup required |
| Security/Compliance | SOC 2 Type II; customer network isolation; audit logging; optional private networking |
| Billing | Pay-by-the-minute; no egress fees; reserved capacity available at 20-40% discounts (1-3 years) |
Pricing & Plans
| Cluster Type | GPU Scale | B200 Rate | H100 Rate | Best For |
|---|---|---|---|---|
| On-Demand | 16-2,000+ | $3.79/GPU-hour | $2.29/GPU-hour | Rapid prototyping, short-term research |
| Reserved 1-Year | 16-2,000+ | $3.49/GPU-hour | $2.19/GPU-hour | Predictable, recurring training (20% savings) |
| Reserved 2-Year | 16-2,000+ | Contact sales | Contact sales | Long-term training (contact sales) |
| Reserved 3-Year | 16-2,000+ | Contact sales | Contact sales | Strategic capacity needs (contact sales) |
Pricing Examples: 16× B200 cluster on-demand: $60.64/hour (~$1,455/day). 100× H100 reserved 1-year: $219/hour (~$1.9M/year). No setup or management fees. Free egress.
Pros & Cons
| Pros (Advantages) | Cons (Limitations) |
|---|---|
| Instant cluster provisioning without long-term contracts: Minutes vs. months for infrastructure; no multi-year commitments required for on-demand pricing. | Costlier than individual instances at small scale: Per-GPU rates higher due to management overhead; savings accrue at 100+ GPU scale. |
| Full InfiniBand performance: Non-blocking fabric eliminates communication bottlenecks, ensuring predictable distributed training efficiency. | Minimum 16-GPU cluster size: Cannot provision <16-GPU clusters; teams needing <8 GPUs must use individual Instances. |
| Pre-installed software stack: Lambda Stack eliminates software provisioning and compatibility issues—training starts within minutes. | No persistent data storage default: External data requires S3, NFS, or other storage integration—adding complexity. |
| Flexible orchestration choice: Support for both Kubernetes and Slurm without architectural compromises. | Limited global availability: 1-Click Clusters availability geographically limited; capacity constraints during peak demand. |
| No egress fees: Free data transfer out of Lambda—enabling cost-effective checkpoint downloads and external integrations. | Multi-year reserved rates not publicly transparent: 2+ year rates require sales engagement; difficult to compare long-term costs. |
| Transparent, predictable billing: Pay-by-the-minute with no surprise charges or usage multipliers. | Orchestration learning curve: Teams unfamiliar with Kubernetes or Slurm require operational expertise. |
Detailed Final Verdict
Lambda 1-Click Clusters represents an optimal balance between infrastructure flexibility and performance for organizations training large AI models without committing to long-term single-tenant infrastructure. The combination of instant provisioning (minutes), full InfiniBand performance, and flexible on-demand pricing solves a critical pain point: enterprises need multi-node capacity for weeks or months, not years, making Supercluster contracts financially wasteful and inflexible. The pre-installed Lambda Stack eliminates software provisioning burden, and managed orchestration removes in-house cluster operations expertise requirements.
However, teams should evaluate cost tradeoffs carefully. At small cluster scales (16-64 GPUs), per-GPU-hour costs exceed individual instances; benefits emerge at larger scales (100+ GPUs) where per-node overhead amortizes. The 16-GPU minimum forces teams needing <8 GPUs to use individual instances, creating fragmented usage. Lack of built-in persistent storage requires external integration. For truly unpredictable GPU needs, RunPod’s autoscaling may offer better flexibility; for 2+ year training, Superclusters’ costs dominate.
Recommendation: 1-Click Clusters is optimal for research teams and enterprises training large models (10B+ parameters) with known durations (days-months) and 16-500 GPU scales. For continuous multi-year production, Lambda Superclusters offer better long-term economics. For sporadic workloads, individual Lambda Instances or RunPod provide better value. For development and prototyping, 1-Click Clusters balance performance, cost, and simplicity unmatched in the market.