
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.
Announcing Runpod Flash
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Skip the setup—launch any package straight from GitHub.


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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
@
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!
@
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!
@
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
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without 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!)
@
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.
@
SaaS Wiz
I love runpod
@
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.
@
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!
@
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.
@
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.
@
Dean Jones
Runpod has great prices as well
@
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! 👀
@
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
@
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
@
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
@
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.
@
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.
@
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!
@
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!)
@
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.
@
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!
@
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
@
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
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
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?
@
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.
@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP
@
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.
@
Dean Jones
Runpod has great prices as well
@
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!
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
SaaS Wiz
I love runpod
@
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.
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod
@
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
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
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! 👀

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

@
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

@
SaaS Wiz
I love runpod

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

@
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.

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

@
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.

@
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

@
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!

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

@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @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

@
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.
.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!

@
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

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

@
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.

@
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
@
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
@
DataEatsWorld
Thanks @runpod_io, loving all of the updates! 👀
@
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
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
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!
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
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
@
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.
@
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.
@
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.
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without 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.
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
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
@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP
@
Dean Jones
Runpod has great prices as well
@
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
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale
@
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?
@
AlicanKiraz0
Runpod > Sagemaker, VertexAi, AzureML
@
qtnx_
1.3k spent on the training run, this latest release would not have been possible without runpod
@
SaaS Wiz
I love 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!)
@
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!
@
berliangor
i'm a big fan of @runpod_io they're most reliable GPU provider for training and running your models at scale
@
abacaj
Runpod support > lambdalabs support. For on demand GPUs runpod still works the best ime
@
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
@
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.
@
Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by @runpod_io 😍
@
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
@
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.
@
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
@
Dean Jones
Runpod has great prices as well
@
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.
@
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!
@
Dwayne
Just discovered @runpod_io 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
@
casper_hansen_
Why is Huggingface not adding Runpod as a serverless provider? Runpod is 10-15x cheaper for serverless deployment than AWS and GCP
@
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.
@
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!
@
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?
@
jzlegion
ai engineering is just tweaking config values in a notebook until you run out of runpod credits

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

@
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 😍

@
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

@
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?

@
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!

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

@
Dean Jones
Runpod has great prices as well

@
SaaS Wiz
I love runpod

@
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.

@
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

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

@
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.

@
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.

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

@
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!

@
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

@
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.
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.

