
qwen / Qwen3 32B AWQ
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
We raised a Series A! Read a post from our CEO, Zhen Lu: 1M devs and the cloud we're building next.
Hub
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Discover, fork, and contribute to community-driven projects.


Skip the setup—launch any package straight from GitHub.


Deploy autoscaling endpoints from community templates.

Runpod Hub is a catalog of templates, models, and open-source AI apps that can be deployed on Runpod. Use Hub to start from a working template, fork an existing project, or publish a reusable deployment path for other developers.
How it Works
Deploy, scale, and manage your entire stack in one streamlined workflow.
Public endpoints
Test, integrate, and deploy without provisioning your own infrastructure.

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.

An image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing and has LoRA support.

The image editing version of Qwen-Image. Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing.

An image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing.

The Deep Cogito v2 Llama 70B model is part of a groundbreaking family of open-source hybrid reasoning LLMs developed under a novel AI paradigm
![black-forest-labs / FLUX.1 [dev]](https://cdn.prod.website-files.com/69de1fe580cd645a46bb3cf1/69e866b4c4a8da2f7ee788e1_68acd0588d67b16bbe7f1a90_black-forest-labs-flux-1-dev.avif)
Offers exceptional prompt adherence, high visual fidelity, and rich image detail.

Fastest and most lightweight FLUX model, ideal for local development, prototyping, and personal use.
![black-forest-labs / FLUX.1 Kontext [dev]](https://cdn.prod.website-files.com/69de1fe580cd645a46bb3cf1/69e866b4c4a8da2f7ee788d5_68accefd4aa2af175d1a8349_black-forest-labs-flux-1-kontext-dev.avif)
FLUX.1 Kontext [dev] is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions.

Seedream 3.0, a native high-resolution bilingual image generation foundational model (Chinese-English).

Latest high-performance video generation model, featuring multi-shot storytelling, strong instruction-following, and semantic understanding.
.avif)
Wan 2.2 is an open-source AI video generation model that utilizes a diffusion transformer architecture and a novel 3D spatio-temporal VAE (Wan-VAE).

Wan 2.2 is an open-source AI video generation model that utilizes a diffusion transformer architecture and a novel 3D spatio-temporal VAE (Wan-VAE) for image-to-video generation.
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
@
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!)
@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale
@
Yoeven
The @runpod_io event was amazing! One reason we can boast about fast speeds at @jigsawstack is because the cold boot on runpod GPUs is basically nonexistent!
@
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
@
SuperHumanEpoch
I have been testing work with @runpod_io last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod
@
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!
@
rachel
thank u runpod i was doing a training run for work when GCP and cloudflare died 🙏🙏 i appreciate u staying online it finished successfully
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
SaaS Wiz
I love runpod
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @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
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀
@
DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Runpod. Yes, it’s possible—and no, you don’t need enterprise hardware.
@
Dean Jones
Runpod has great prices as well
@
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?
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits
@
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.
@
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.
@
winglian
Axolotl works out of the box with @runpod_io's Clusters. It's as easy as running this on each node using the Docker images that we ship.
@
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!
@
SuperHumanEpoch
I have been testing work with @runpod_io last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.
@
winglian
Axolotl works out of the box with @runpod_io's Clusters. It's as easy as running this on each node using the Docker images that we ship.
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
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.
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
Yoeven
The @runpod_io event was amazing! One reason we can boast about fast speeds at @jigsawstack is because the cold boot on runpod GPUs is basically nonexistent!
@
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?
@
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
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits
@
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!
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
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.
@
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!
@
DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Runpod. Yes, it’s possible—and no, you don’t need enterprise hardware.
@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale
@
rachel
thank u runpod i was doing a training run for work when GCP and cloudflare died 🙏🙏 i appreciate u staying online it finished successfully
@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP
@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀
@
Dean Jones
Runpod has great prices as well
@
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
@
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!)
@
SaaS Wiz
I love runpod
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod

@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale

@
SuperHumanEpoch
I have been testing work with @runpod_io last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.

@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP

@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod

@
winglian
Axolotl works out of the box with @runpod_io's Clusters. It's as easy as running this on each node using the Docker images that we ship.

@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀

@
DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Runpod. Yes, it’s possible—and no, you don’t need enterprise hardware.

@
Dean Jones
Runpod has great prices as well

@
rachel
thank u runpod i was doing a training run for work when GCP and cloudflare died 🙏🙏 i appreciate u staying online it finished successfully

@
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!

@
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.

@
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.

@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits

@
SaaS Wiz
I love runpod

@
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?

@
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
.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!)

@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime

@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat

@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
.avif)
@
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!

@
Yoeven
The @runpod_io event was amazing! One reason we can boast about fast speeds at @jigsawstack is because the cold boot on runpod GPUs is basically nonexistent!

@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML

@
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
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
winglian
Axolotl works out of the box with @runpod_io's Clusters. It's as easy as running this on each node using the Docker images that we ship.
@
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?
@
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!)
@
SuperHumanEpoch
I have been testing work with @runpod_io last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.
@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP
@
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
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale
@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀
@
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.
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
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.
@
rachel
thank u runpod i was doing a training run for work when GCP and cloudflare died 🙏🙏 i appreciate u staying online it finished successfully
@
DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Runpod. Yes, it’s possible—and no, you don’t need enterprise hardware.
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
Yoeven
The @runpod_io event was amazing! One reason we can boast about fast speeds at @jigsawstack is because the cold boot on runpod GPUs is basically nonexistent!
@
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
@
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!
@
SaaS Wiz
I love runpod
@
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!
@
Dean Jones
Runpod has great prices as well
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale
@
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.
@
SuperHumanEpoch
I have been testing work with @runpod_io last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.
@
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!
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod
@
DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Runpod. Yes, it’s possible—and no, you don’t need enterprise hardware.
@
Yoeven
The @runpod_io event was amazing! One reason we can boast about fast speeds at @jigsawstack is because the cold boot on runpod GPUs is basically nonexistent!
@
Dean Jones
Runpod has great prices as well
@
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!)
@
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!
@
rachel
thank u runpod i was doing a training run for work when GCP and cloudflare died 🙏🙏 i appreciate u staying online it finished successfully
@
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
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits
@
winglian
Axolotl works out of the box with @runpod_io's Clusters. It's as easy as running this on each node using the Docker images that we ship.
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀
@
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
@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP
@
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?
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
SaaS Wiz
I love runpod
@
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.

@
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?

@
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.
.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!)

@
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

@
SuperHumanEpoch
I have been testing work with @runpod_io last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.

@
rachel
thank u runpod i was doing a training run for work when GCP and cloudflare died 🙏🙏 i appreciate u staying online it finished successfully

@
Dean Jones
Runpod has great prices as well

@
DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Runpod. Yes, it’s possible—and no, you don’t need enterprise hardware.

@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime

@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP

@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits

@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat

@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀

@
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.

@
SaaS Wiz
I love runpod
.avif)
@
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 Clusters. It's as easy as running this on each node using the Docker images that we ship.

@
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!

@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale

@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML

@
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

@
Yoeven
The @runpod_io event was amazing! One reason we can boast about fast speeds at @jigsawstack is because the cold boot on runpod GPUs is basically nonexistent!

@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod

@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
FAQs
Serverless, simplified. Clear answers on running your code without the fuss.
Runpod Hub is a centralized catalog of preconfigured AI repositories that you can browse, deploy, and share. All repos are optimized for Runpod’s Serverless infrastructure, so you can go from discovery to a running endpoint in minutes.
No—the Hub is currently in beta. We’re actively adding features and fixing bugs. Join our Discord if you’d like to give feedback or report issues.
One-click deployment: All Hub repos come with prebuilt Docker images and Serverless handlers. You don’t have to write Dockerfiles or manage dependencies.
Configuration UI: We expose common parameters (environment variables, model paths, precision settings, etc.) so you can tweak a repo without touching code.
Built-in testing: Every repo in the Hub has automated build-and-test pipelines. You can trust that the code runs properly on Runpod before you click “Deploy.”
Save time: Instead of cloning a repo, installing dependencies, and debugging runtime issues, you can launch a vetted endpoint in minutes.
End users/Developers: Quickly find and run popular AI models (LLMs, Stable Diffusion, OCR, etc.) without setup headaches. Customize inputs via a simple form instead of editing code.
Hub creators: Showcase your open-source work to the Runpod community. Every new GitHub release triggers an automated build/test cycle in our pipeline, ensuring your repo stays up to date.
Enterprises/Teams: Adopt standardized, production-ready AI endpoints without reinventing infrastructure. Onboard developers faster by pointing them to Hub listings rather than internal deployment docs.
In the Runpod console, go to the Hub page.
Browse or search for a repo that matches your needs.
Click on the repo to view details—check hardware requirements (CPU vs. GPU, disk size) and any exposed configuration options.
Click Deploy (or choose an older version via the dropdown).
Click Create Endpoint. Within minutes, you’ll have a live Serverless endpoint you can call via API.
For a more details, check out the docs: https://docs.runpod.io/hub/overview.
Prepare a working Serverless implementation in your GitHub repo. You’ll need a handler.py (or equivalent), a Dockerfile, and a README.md.
Add a .runpod/hub.json file with metadata (title, description, category, hardware settings, environment variables, presets).
Add a .runpod/tests.json file that defines one or more test cases to exercise your endpoint (each test should return HTTP 200).
Create a GitHub Release (the Hub indexes releases rather than commits).
In the Runpod console, go to the Hub and click Get Started under “Add your repo.” Enter your GitHub URL and follow the prompts.
Once submitted, our build pipeline will automatically scan, build, and test your repo. After it passes, our team will manually review it. If approved, your repo appears live in the Hub.
For a more details, check out the docs: https://docs.runpod.io/hub/publishing-guide.
Clients
Engineered for teams building the future.
Build, train, and scale AI workloads on Runpod with cloud GPUs, Serverless, and Clusters.

