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AI Cloud Infrastructure🔴Developer
L

Lambda

GPU cloud for AI training and inference offering on-demand and reserved Nvidia H100, H200, B200, and A100 instances at competitive per-hour rates.

Starting atPer-hour by GPU
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💡

In Plain English

GPU cloud for AI training and inference offering on-demand and reserved Nvidia H100, H200, B200, and A100 instances at competitive per-hour rates.

OverviewFeaturesPricingUse CasesFAQ

Overview

Lambda (lambda.ai, formerly Lambda Labs) is one of the original AI-specialized GPU clouds. Researchers and ML engineers use Lambda for end-to-end training, fine-tuning, and inference on Nvidia hardware ranging from older A100 80GB instances to current-generation H100, H200, and B200 systems. The product line spans 1-Click Clusters for multi-node training, Cloud (on-demand single-instance GPUs), Reserved Cloud (committed capacity for production workloads), and Private Cloud (dedicated single-tenant infrastructure). Unlike serverless platforms, Lambda exposes raw VMs you SSH into, which makes it well-suited to training runs that need full control over the environment and persistent storage. Lambda also operates a managed inference API for popular open models and sells on-prem AI hardware appliances. The site emphasizes high availability of cutting-edge GPUs at a time when H100/H200/B200 supply elsewhere is constrained. Per-hour pricing varies by GPU class and region; exact rates were not fully extracted from the marketing pages and need verification on the GPU Cloud pricing page. Lambda is a strong fit for teams who want VM-level GPU access with predictable hourly billing rather than per-second serverless.

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Key Features

Feature information is available on the official website.

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Pricing Plans

On-Demand Cloud

Per-hour by GPU

    Reserved Cloud / 1-Click Clusters

    Discounted committed capacity

      Private Cloud / On-Prem

      Contact sales

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        Best Use Cases

        🎯

        Fine-tuning and training open models on dedicated GPUs

        ⚡

        Researchers needing persistent VM access with raw SSH

        🔧

        Teams committing to reserved GPU capacity for production

        🚀

        On-prem AI deployments

        Pros & Cons

        ✓ Pros

        • ✓Cutting-edge GPU availability (H200/B200) when hyperscalers are constrained
        • ✓Raw VM access with SSH/root — full control of environment and CUDA stack
        • ✓Reserved pricing is meaningfully cheaper than AWS/GCP for the same silicon
        • ✓1-Click Clusters remove the InfiniBand wiring pain for multi-node training

        ✗ Cons

        • ✗Not serverless — you pay for the VM whether it's busy or idle
        • ✗Less mature platform tooling than hyperscalers (smaller managed-services menu)
        • ✗Public per-hour rates aren't in one easy table; verification needed
        • ✗Cold starts of new on-demand capacity can take minutes during supply crunches

        Frequently Asked Questions

        How much does Lambda cost?+

        Lambda pricing starts at Per-hour by GPU. They offer 3 pricing tiers.
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        Quick Info

        Category

        AI Cloud Infrastructure

        Website

        lambda.ai/
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