Built for open source.
Discover, fork, and contribute to community-driven projects.
One-click deployment.
Skip the setup—launch any package straight from GitHub.
Everything in one place.
Read docs, explore files, and track GitHub metrics—all in Hub.
How it Works
From code to cloud.
Deploy, scale, and manage your entire stack
in one streamlined workflow.
Templates
Find your next build.
Explore hundreds of official and community-built templates, ready to deploy in seconds.
Community
Join the community.
Build, share, and connect with thousands
@
casper_hansen_
Why is Huggingface not adding RunPod as a serverless provider? RunPod is 10-15x cheaper for serverless deployment than AWS and GCP
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits
@
roland_graser
switching from runpod+cursor to colab made my ml pipeline setup 100x faster, but i'm missing access to claude sonnet 3.7 for interpreting repo files & terminal outputs. need a way to either: 1. connect cursor to colab so the llm sees my terminal outputs
2. efficiently feed colab outputs to an llm without tedious copy/paste what's the best workflow for llm-assisted debugging when your code runs in colab but your preferred assistant lacks context? any MCP servers to give cursor access to colab?
@
dfranke
Shoutout to @runpod_io as I work through my first non-trivial machine learning experiment. They have exactly what you need if you're a hobbyist and their prices are about a fifth of the big cloud providers.
.webp)
@
Mascobot
Apparently, we got a Kaggle silver medal in the @arcprize for being in position 17th out of 1430 teams 🙃 I wish I had more time to spend on it; we worked on it for a couple of weeks for fun with limited compute (HUGE thanks to @runpod_io!)
@
SkotiVi
For anyone annoyed with Amazon's (and Azure's and Google's) gatekeeping on their cloud GPU VMs, I recommend @runpod_io None of the 'prove you really need this much power' bs from the majors Just great pricing, availability, and an intuitive UI
.webp)
@
skypilot_org
🏃 RunPod is now available on SkyPilot! ✈️ Get high-end GPUs (3x cheaper) with great availability: sky launch --gpus H100 Great thanks to @runpod_io for contributing this integration to join the Sky!
@
winglian
Axolotl works out of the box with @runpod_io's Instant Clusters. It's as easy as running this on each node using the Docker images that we ship.
@
YuvrajS9886
Introducing SmolLlama! An effort to make a mini-ChatGPT from scratch! Its based on the Llama (123 M) structure I coded and pre-trained on 10B tokens (10k steps) from the FineWeb dataset from scratch using DDP (torchrun) in PyTorch. Used 2xH100 (SXM) 80GB VRAM from Runpod
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
othocs
@runpod_io is so goated, first time trying it today and it’s super easy to setup + their ai helper on discord was very helpful If you ever need cpus/gpus I recommend it!
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
oliviawells
Needed a GPU for a quick job, didn’t want to commit to anything long-term. RunPod was perfect for that. Love that I can just spin one up and shut it down after.
FAQs
Questions? Answers.
RunPod Hub explained.
What sets RunPod’s serverless apart from other platforms?
RunPod’s serverless GPUs eliminate cold starts with always-on, pre-warmed instances, ensuring low-latency execution. Unlike traditional serverless solutions, RunPod offers full control over runtimes, persistent storage options, and direct access to powerful GPUs, making it ideal for AI/ML workloads.
What programming languages and runtimes are supported?
RunPod supports Python, Node.js, Go, Rust, and C++, along with popular AI/ML frameworks like PyTorch, TensorFlow, JAX, and ONNX. You can also bring your own custom runtime via Docker containers, giving you full flexibility over your environment.
How does RunPod reduce cold-start delays?
RunPod uses active worker pools and pre-warmed GPUs to minimize initialization time. Serverless instances remain ready to handle requests immediately, preventing the typical delays seen in traditional cloud function environments.
How are deployments and rollbacks managed?
RunPod allows deployments directly from GitHub, with one-click launches for pre-configured templates. For rollback management, you can revert to previous container versions instantly, ensuring a seamless and controlled deployment process.
How does RunPod handle event-driven workflows?
RunPod integrates with webhooks, APIs, and custom event triggers, enabling seamless execution of AI/ML workloads in response to external events. You can set up GPU-powered functions that automatically run on demand, scaling dynamically without persistent instance management.
What tools are available for monitoring and debugging?
RunPod offers a comprehensive monitoring dashboard with real-time logging and distributed tracing for your serverless functions. Additionally, you can integrate with popular APM tools for deeper performance insights and efficient debugging.
Clients
Trusted by today's leaders, built for tomorrow's pioneers.
Engineered for teams building the future.
