Lambda
Lambda · Image · Coding
Lambda is a GPU cloud platform for AI developers. It rents NVIDIA GPU instances by the hour, plus ready-made clusters for training and inference.
About Lambda
What Is Lambda
Lambda is an AI training cloud that lets you rent GPU by the hour, so you can train and run large models without buying your own hardware. The company started in 2012 and runs data centers built for AI workloads. Developers rent single machines or whole clusters, then connect through the web dashboard, a command line tool, or the Lambda Cloud API.
The main draw is simplicity and price. Instead of tuning a generic virtual machine, you launch an instance that already has the drivers and machine learning libraries installed. Pricing sits in plain view, so you know the cost per GPU per hour before you start. That matters when a training run can tie up eight GPUs for days.
Two limits matter up front. On-demand capacity sells out, especially for the newest chips, so a fast experiment sometimes has to wait for hardware to free up. And Lambda is built for people who write code. There's no drag-and-drop studio, and no free tier to poke around in before you commit. Reserved clusters and long contracts are the path to the lowest rates.
Getting Started
- Create a Lambda account and add a payment method in the cloud dashboard.
- Pick a GPU instance (from one to eight GPUs) or request a 1-Click Cluster for larger jobs.
- Launch the instance, then connect over SSH or the web terminal. Lambda Stack is already installed.
- Move your code and data over, run your training or inference job, and shut the instance down when you're done.
Product Information
A quick look at Lambda's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Machine learning engineers
- AI startups
- Researchers and academics
- Data scientists
Tasks
- Training and fine-tuning large models
- Serving inference at scale
- Prototyping and experiments
- Benchmarking
Scenarios
- A student renting a single A100 for a weekend course project
- A research team launching a 16-GPU cluster for a two-week training sprint
- A startup deploying an open model for a product feature
- A developer who needs a quick GPU for a notebook
Key features
On-Demand GPU Instances
You pick a machine and it's yours in minutes, billed per GPU per hour. Options cover the range from a single Tesla V100 up to eight NVIDIA B200 or H100 GPUs, with storage and RAM matched to each tier. The on-demand pricing is the same whether you run one hour or one month, so short jobs don't carry a hidden premium.
1-Click Clusters for Large Jobs
1-Click Clusters link 16 to 2,000+ NVIDIA B200 or H100 GPUs over InfiniBand, which is the fast network you need when work is spread across many machines. You reserve a cluster from the dashboard for anywhere from two weeks to a year. This is the tier built for training foundation models, not for small experiments.
Lambda Stack Preinstalled
Every instance ships with Lambda Stack, a tested bundle of the software most AI work depends on, including PyTorch, TensorFlow, CUDA, and cuDNN. The bundle is checked for compatibility across NVIDIA generations, so you spend less time fixing version mismatches and more time on models. It also installs on your own Ubuntu box if you want the same setup locally.
Lambda Cloud API
The REST API lets you create, stop, and restart instances from your own scripts, CI pipelines, or orchestration tools. You can automate a fleet of machines without clicking through a dashboard, which is how teams keep training jobs repeatable. Requests are rate limited, so heavy automation should be paced.
Management and Orchestration Options
Lambda supports Kubernetes and Slurm for scheduling jobs across GPUs, along with open tools like SkyPilot and dstack. Managed options exist if you'd rather not run the scheduler yourself. The point is scheduling: keeping GPUs busy instead of idle cuts the cost of a large job.
Trust and Security Controls
Dedicated, single-tenant setups keep your data off shared hardware, and the platform holds SOC 2 Type II and ISO certifications. Caged clusters add hardware-level isolation for sensitive work. For teams handling regulated or proprietary data, this is often the deciding factor over a cheaper shared option.
Pros and cons
Pros
- Clear per-GPU hourly pricing makes a training budget easy to estimate before you commit.
- Lambda Stack removes the usual CUDA and driver setup, so instances work within minutes of launch.
- The lineup runs from a single affordable GPU to thousands of B200 or H100 chips in one place.
- The REST API and orchestrators like Kubernetes and Slurm support automated, repeatable training pipelines.
- SOC 2 Type II and ISO certifications and single-tenant setups fit teams with data compliance needs.
Cons
- New GPUs sell out fast, so on-demand launches can fail during peak demand and force a wait.
- There's no free tier, so you can't test the platform without adding a payment method.
- The workflow assumes you can write and run code over SSH or the API, which rules out non-technical users.
- The Inference API is being retired, so hosted endpoints won't be an option going forward.
Frequently asked questions
Lambda rents cloud GPUs for AI work. People use it to train, fine-tune, and run large models without owning the hardware. You pick a GPU instance, connect over SSH or the API, and pay by the hour.
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