Status: Image is up to date for runpod/pytorch:3.10-2.0.0-117
6
1970-01-01T00:00:06.000Z
start container
Spin up a GPU pod in seconds
it's a pain to having to wait upwards of 10 minutes for your pods to spin up - we've cut the cold-boot time down to milliseconds, so you can start building within seconds of deploying your pods.
Respond to user demand in real time with GPU workers that scale from 0 to 100s in seconds.
Flex
Workers
Active
Workers
10 GPUs
6:24AM
100 GPUs
11:34AM
20 GPUs
1:34PM
Usage Analytics
Real-time usage analytics for your endpoint with metrics on completed and failed requests. Useful for endpoints that have fluctuating usage profiles throughout the day.
Debug your endpoints with detailed metrics on execution time. Useful for hosting models that have varying execution times, like large language models. You can also monitor delay time, cold start time, cold start count, GPU utilization, and more.
"There are definitely providers who offer much cheaper pricing than Runpod. But they always have an inferior developer experience. If you're paying 50% less for a GPU elsewhere, that cost is coming out somewhere else, be it developer time or lack of reliability. For the value, Runpod provides competitive prices and we're willing to pay a premium to reduce the headache that normally comes with ML ops."
"The setup process was great! Very quick and easy. RunPod had the exact GPUs we needed for AI inference and the pricing was very fair based on what I saw out on the market. The main value proposition for us was the flexibility RunPod offered. We were able to scale up effortlessly to meet the demand at launch."
"The cost savings on RunPod have been incredible. Since switching, our team has been able to focus on building the product instead of the infrastructure.
We often have unpredictable demand from our users which makes it hard to manage our cloud costs. But with RunPod, we've been able to scale up and down quickly and painlessly.
Great reliability in multiple regions and great customer support is why we've been with them for over a year now."
We handle millions of inference requests a day. Scale your machine learning inference while keeping costs low with RunPod serverless.
AI Training
Run machine learning training tasks that can take up to 7 days. Train on our available NVIDIA H100s and A100s or reserve AMD MI300Xs and AMD MI250s a year in advance.
Autoscale
Serverless GPU workers scale from 0 to n with 8+ regions distributed globally. You only pay when your endpoint receives and processes a request.
Bring Your Own Container
Deploy any container on our AI cloud. Public and private image repositories are supported. Configure your environment the way you want.
Zero Ops Overhead
RunPod handles all the operational aspects of your infrastructure from deploying to scaling. You bring the models, let us handle the ML infra.
Network Storage
Serverless workers can access network storage volume backed by NVMe SSD with up to 100Gbps network throughput. 100TB+ storage size is supported, contact us if you need 1PB+.
Easy-to-use CLI
Use our CLI tool to automatically hot reload local changes while developing, and deploy on Serverless when you’re done tinkering.
Secure & Compliant
RunPod AI Cloud is built on enterprise-grade GPUs with world-class compliance and security to best serve your machine learning models.
Lightning Fast Cold-Start
With Flashboot, watch your cold-starts drop to sub 250 milliseconds. No more waiting for GPUs to warm up when usage is unpredictable.
Launch your AI application in minutes
Start building with the most cost-effective platform for developing and scaling machine learning models.