The AI Cloud.

From GPU VMs and clusters to model deployments and models as APIs. Build at any layer, on one platform.

Trusted worldwide

27,000+AI developers

50M+GPU hours served

<90sInstance launch

Products

One platform, from a GPU to an endpoint.

Same GPUs underneath. Pick how much of the stack you want to own.

Compute

Root-access VMs, ready-to-run Templates, and clusters up to thousands of GPUs.

GPU VMs

Full root access

Dedicated GPU VMs with root SSH from minute one. Run your own Docker or Kubernetes, custom kernels and drivers, up to 8 GPUs per VM.

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Templates

Ready-to-run environments

PyTorch, ComfyUI and more, pre-built and live in 1.8 seconds. Work over JupyterLab, VS Code or SSH, with storage that persists across stop and start.

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GPU Clusters

Multi-node training

Launch an Instant Cluster from the dashboard, or reserve 128 to 1,024+ GPUs on InfiniBand with capacity confirmed by our team.

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Shared Filesystems

Storage across instances

Network storage that mounts on any number of your instances, so datasets and checkpoints outlive any single machine.

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Inference

Deploy models as API endpoints, on our managed stack or your own.

Managed Endpoints

Deploy open models

Choose a model from the catalog and get an API endpoint. We maintain the GPU configuration, serving image, precision and kernels.

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Serverless

Bring your own serving stack

Deploy your own model on vLLM, SGLang or Ollama. Workers scale to zero, with logs, metrics and usage built in. Pay per minute of worker runtime.

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Compare Templates and VMs

Security

Isolate what you run.

Private networks, firewall rules and reserved addresses for your instances, isolated from every other tenant.

Private networkingPut your instances inside your own VPC, on IP ranges you choose, isolated from every other tenant.
Security groupsFirewall rules for your VMs, inbound and outbound. Open SSH to your IP only, keep everything else closed.
Reserved IPsHold a public IP and launch VMs on it. Pause and resume as often as you like; the address your clients point at stays the same.

CLI & SDK

Your GPUs, from the terminal.

Templates, VMs, filesystems and serverless deployments from one CLI or Python, with skills for Claude Code built in.

Explore the CLI

~/projectCLI demo

$ pip install jarvislabs

$ jl create --gpu H100

✓ Instance ready in 38s

$ jl run train.py --gpu H100

⠋ Uploading code and starting training...

jl create --gpu H100 --vmLaunch a Template or a root-access VM. Multi-GPU and VPC placement are flags.
jl run train.py --gpu L4Upload your project, run it on a fresh GPU and stream the logs back.
jl ssh <id>SSH in, execute a command, or upload and download files without leaving the terminal.
jl deploy createCreate a serverless deployment and follow its logs live.
jl filesystem · jl vpcManage filesystems and private networks alongside your instances.
jl setupAuthenticate once and install agent skills, so Claude Code or Codex can drive your GPUs.

Testimonials

Loved by AI practitioners.

Thousands of researchers, engineers, and teams trust Jarvislabs for their GPU workloads.

“

“The cheapest would probably be Jarvis Labs. They're very popular in our community. https://jarvislabs.ai”

Jeremy Howard

Founder, fast.ai

“

“Looking to run a bigger model on GPU at a cheaper price, give @jarvislabsai a try and thank me later 😀 Got my machine up and running in a few mins 🔥 Thank you @vishnuvig!”

SRK

@sudalairajkumar

“

“If you haven't tried http://jarvislabs.ai you should. Fast start times, well priced, simple UI, easy billing. I have always chosen between config crap, expensive price, or long launch times. This is the first platform that I've seen that I think gets all of these items right!”

Isaac Flath

@isaac_flath

“

“The incredible https://jarvislabs.ai by @vishnuvig IMO offers one of the best pricing for renting compute 💰 TIL that they are completely bootstrapped & operate out of India! 🙏 It's really fulfilling to hear one of the best startups from @fastdotai classroom is from the country!”

Sanyam Bhutani

@bhutanisanyam1

“

“@jarvislabsai is the best GPU cloud provider for DL practitioners out there, period. More than once I had a question and support helped me in minutes, not only fast but so so friendly...”

Lucas Vazquez

@vzzqzzz

“

“Addict to @jarvislabsai. Less branded than others on the surface but super simple. Great GPUs (training on 8 x A100s is amazing). This beats Paperspace premium accounts, Colab with custom VMs... I loved RunwayML as well ...”

Laurent Thevenet

@thelau

Customer stories

Built for production workloads.

See how AI teams use Jarvislabs from the first training run to always-on, demand-driven inference.

ZYNG AIE-commerce image operations

How ZYNG AI edits 5 million images a month on Jarvislabs

One GPU cloud for model training, always-on inference, and automated capacity that follows demand.

Read the customer story

5M+images edited every month

3production compute modes

~10%higher throughput reported

Pricing

Pay as you go.

Pay by the minute with no minimum commitment, and only for storage while paused.

Explore Pricing

Hourly Pricing

GPUs

NVIDIA H200 SXM

$3.99/hour

NVIDIA H100 SXM

$2.69/hour

NVIDIA RTX Pro 6000

$1.89/hour

NVIDIA A100-80GB

$1.49/hour

Storage

Instance Storage

$0.00014/GB/hour

FAQ

Frequently asked questions

Can't find what you're looking for? Reach out to our support team.

Sign up, top-up your credits, select your preferred GPU (e.g., A100, H100), and launch your instance. Within minutes, you’ll have a fully configured environment ready for AI training or any GPU-accelerated workload.

We don’t offer a free trial, but you can start exploring with as little as $10. Our pay-as-you-go model ensures you only pay when you rent a GPU and use it.

You’re charged only for the time your rented GPU instance is running. When paused, you pay solely for storage. Our per-minute billing means no hidden fees or commitments.

A template is a pre-built container with a primary framework (e.g., PyTorch, TensorFlow). After you rent a GPU, you can install custom packages via apt or pip to suit your project.

A template is a managed container — we handle the OS, drivers, CUDA, and the runtime, and you bring your code. A VM is a full machine with root access from minute one, so you can run your own Docker or Kubernetes, load custom kernels and drivers, and install anything a container can’t host. Both use the same GPUs at the same hourly rate.

Yes — launch a VM. You get root SSH immediately and full control of the operating system, including custom kernel modules, your own container runtime, and your own monitoring or security agents.

Yes. Bring your own model on vLLM, SGLang, or Ollama and get an endpoint URL backed by an autoscaling worker pool that scales to zero, with logs, metrics, and usage built in. You pay per minute of worker runtime, and you can deploy one from the dashboard after signing up.

Managed Endpoints are open models from our catalog that you deploy as an API endpoint. We select and maintain the GPU configuration, serving image, precision, and kernels; you choose the model and how it scales. Serverless is the same autoscaling worker pool with your own serving stack, for when you need control over the framework and model. Both bill per minute of worker runtime.

Yes. Launch an Instant Cluster from the dashboard, with NVIDIA H200 available today and more GPU options being added. For 128, 256, 1,024 GPUs and beyond, reserve a cluster built on NVIDIA reference architecture with InfiniBand; our sales team confirms capacity and delivery dates.

Yes. You can create a VPC with your own private IP ranges and run your instances inside it, isolated from other tenants’ traffic. VPCs are currently available in our India regions.

Once you rent a GPU, instances typically launch in seconds. Start coding immediately via JupyterLab, VS Code Web, or SSH.

Your rented GPU instances run in isolated environments with private networking, ensuring proper data isolation and security.

Pausing stops GPU usage charges. You pay only for storage while it’s paused. Resume anytime to continue working where you left off.

Deletion is permanent, and data cannot be recovered. After deletion, no further charges apply.

We currently offer GPU rentals in India and Europe, with more regions coming soon.

We provide technical support related to the entire lifecycle of your rented GPU instance. While we do our best to address framework-specific queries, we can’t guarantee support for all open-source tools.

We use Stripe for secure payments. Pay with credit/debit cards or net banking, ensuring a smooth transaction process.

Not currently. We support Stripe transactions at this time.

We don’t offer refunds. If you have a valid reason, email us at engineering@jarvislabs.ai, and we’ll review your request.

Get started

Start building on The AI Cloud.

From your first GPU to production inference. Find your starting point.