Lambda
Train and scale AI on NVIDIA VR200 NVL 72, GB300 NVL 72, B300, B200, H200, H100, and more GPUs.
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Lambda provides on-demand and reserved GPU instances, clusters, and superclusters for training and inference on high-density NVIDIA hardware.
- for
- AI researchers, ML engineers, and enterprises needing large-scale GPU compute.
- pricing
- paid
Key features
- On-demand GPU Instances — Spin up NVIDIA B200, H100, A100, GH200, and other instances in minutes via self-serve portal.
- 1-Click Clusters™ — Launch production-ready multi-GPU clusters (B200 or H100) with a single click, optimized for distributed training.
- Superclusters — Reserve single-tenant NVIDIA GB300 NVL72 clusters with Quantum-2 InfiniBand for massive scale.
- Secure Single-Tenant Architecture — Data isolation with shared-nothing design and SOC 2 Type II compliance.
- Expert Orchestration — Managed clusters and co-engineering support from Lambda’s infrastructure team.
- High-Density Power & Liquid Cooling — Rack-scale systems with advanced cooling for peak AI performance.
Use cases
- Train foundation models on hundreds of GPUs
- Serve billions of tokens per second for inference
- Prototype AI workloads on single GPU instances
- Run large-scale distributed training without managing hardware
- Maintain compliance-ready environments for sensitive data
Lambda pricing
- NVIDIA H100 Instance$3.99/GPU hour80 GB VRAM · 208 vCPUs · 22 TiB SSD
- NVIDIA A100 Instance$2.79/GPU hour80 GB VRAM · 240 vCPUs · 19.5 TiB SSD
- NVIDIA H100 1-Click Cluster (64 GPUs)$5.85/GPU hourProduction-ready cluster · Optimized for distributed AI
- NVIDIA B200 Supercluster (256+ GPUs)$8.87/GPU hourSingle-tenant · Quantum-2 InfiniBand
Lambda vs alternatives
 Lambda |  Runpod |  Modal | |
|---|---|---|---|
| Best for | Scalable, secure GPU clusters for AI workloads | On-demand GPU compute marketplace | Serverless GPU functions |
| Pricing | Paid | Subscription | Subscription |
| DevHunt upvotes | 0 | 0 | 0 |
| Launched | — | — | — |
- Lambda vs Runpod: Runpod offers on-demand GPU instances but focuses on a broader marketplace, lacking Lambda’s single-tenant superclusters and built-in orchestration support.
- Lambda vs Modal: Modal provides serverless GPU workloads via Python, whereas Lambda delivers full VM/cluster control with dedicated hardware and compliance features.
Lambda FAQ
How do I start using Lambda’s GPU resources?+
Click “Launch GPU instance” on the homepage, sign up, and provision an instance or cluster instantly.
What hardware options are available?+
Lambda offers NVIDIA VR200 NVL72, GB300 NVL72, HGX B200, H100, A100, GH200, and other GPUs across instances, clusters, and superclusters.
Is the infrastructure compliant for sensitive data?+
Yes, it uses a single-tenant, shared-nothing architecture and holds SOC 2 Type II certification.
Can I get assistance with cluster setup?+
Lambda provides managed clusters and co-engineering support from the team that built the infrastructure.
How is pricing calculated?+
Pricing is per GPU-hour (e.g., $3.99/GPU/hr for H100 instances) plus applicable taxes; reserved capacity pricing is available on request.
Summarized by DevHunt from lambda.ai · Nov 4, 2026. Details may change; check the official site.
About this listing
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